Reversecore MCP

Official

by sjkim1127 · Python

Security-first MCP server for reverse engineering, malware analysis, forensics, and SAST.

Reversecore MCP (Model Context Protocol)

Reversecore MCP is a security-first MCP server focused on reverse engineering, malware analysis, digital forensics, and SAST. It exposes capabilities to AI assistants via Model Context Protocol, allowing natural-language requests for tasks including vulnerability research and source code auditing.

🛠️ Key Features

Security-first scope covering reverse engineering and malware analysis
Supports digital forensics and source code auditing
Targets vulnerability research and SAST
Categorized for MCP and security workflows (topics include mcp-server, mcp, mcp-security, sast)

🚀 Use Cases

Malware analysis and related reverse engineering tasks
Digital forensics investigations
Vulnerability research and SAST-oriented review
Source code auditing

⚡ Developer Benefits

MCP-based integration for AI assistants (e.g., Claude, Cursor mentioned in the excerpt)
Aligns with existing reverse-engineering tool ecosystems referenced by topics (Ghidra, radare2)

⚠️ Limitations

Tooling details, interfaces, and counts are not provided in the available excerpt

Topics

mcp-serverreverse-engineeringmcpghidraradare2securitymcp-securitysast

README.md69 KBView on GitHub

Reversecore MCP

AI-Powered Reverse Engineering & Security Analysis via Model Context Protocol

An MCP server that gives AI assistants like Claude and Cursor the ability to perform reverse engineering, malware analysis, vulnerability research, digital forensics, and source code auditing through natural language.

Tests Coverage OpenSSF Scorecard

Watch the Demo

What is Reversecore MCP?
Architecture
Tool Catalog (151 Tools)
Guided Analysis Prompts (22 Modes)
MCP Resources (11 URIs)
Quick Start
Connect to Your AI Client
Configuration
Security Model
Development
CI/CD Pipeline
Docker Build Architecture
System Requirements
Project Structure
Error Handling
Adding New Tools
Contributing
Documentation
License

What is Reversecore MCP?

Reversecore MCP is a Model Context Protocol server that wraps 151 analysis tools into a single interface that AI assistants can call through natural language.

Instead of learning the command-line syntax for a dozen different tools, you describe what you want:

code

"Decompile the main function of this malware sample, extract all network IOCs,
 map the behavior to MITRE ATT&CK, and generate a triage report."

The AI assistant breaks this into tool calls:

code

r2_decompile("sample.exe", "main")
  → extract_iocs("sample.exe")
    → add_mitre_technique(technique_id="T1071.001", ...)
      → create_analysis_report(template_type="quick_triage")

Each tool returns a structured ToolResult (either ToolSuccess or ToolError) with typed data that the AI can reason about, chain into follow-up queries, or render for the user.

What it covers

Domain	What you can do
Static analysis	Disassembly, decompilation (r2ghidra), binary parsing (LIEF), packer detection (DIE), capability detection (CAPA), string extraction, firmware scanning (binwalk)
Dynamic & symbolic	ESIL emulation, angr symbolic execution, taint analysis, fuzzing harness generation
Malware analysis	IOC extraction, YARA scanning, dormant backdoor detection, adaptive vaccine generation, autonomous vulnerability hunting
Vulnerability research	Dangerous API detection, ROP gadget discovery, heap exploit analysis, crash triage, PoC generation
Digital forensics	Memory forensics (Volatility3), PCAP analysis (Scapy), disk forensics (Sleuth Kit), artifact correlation
Source code audit	Python AST scanning, C/C++ regex pattern scanning
Reporting	Session-based reports with MITRE ATT&CK mapping, SIGMA rule generation, VEX reports, email delivery

Architecture

code

AI Client (Claude / Cursor / any MCP-compatible client)
        │  MCP Protocol (stdio or HTTP/SSE)
        ▼
┌──────────────────────────────────────────────────────┐
│                   FastMCP 3.4.4 Server               │
│          151 registered tools · Fully async          │
│                  Python 3.10–3.12                    │
├────────────────────┬─────────────────────────────────┤
│   Guided Prompts   │  Dynamic Resources              │
│  (22 analysis      │  (11 URI-based: per-binary      │
│   modes)           │   strings, IOCs, ASM, CFG, …)   │
├────────────────────┴─────────────────────────────────┤
│                  Core Infrastructure                 │
│  Config · Security · Validators · Exceptions (17)    │
│  R2 Pool · Metrics · Memory (SQLite) · Task Queue    │
│  MITRE Mapper · Evidence Engine · Resilience Layer   │
│  Arch Registry (x86/ARM/MIPS/RISC-V/PPC)            │
│  Result Cache (SHA256) · Analysis Cache (Redis+SQL)  │
│  SAST (Python AST + C/C++ Regex) · Plugin System     │
├──────────────────────────────────────────────────────┤
│                 Analysis Engines                     │
│  Radare2 6.0.4     │  YARA 4.3.1 · LIEF · Capstone  │
│  r2ghidra           │  CAPA · angr · Qiling          │
│  Volatility3 · Scapy│ DIE · Binwalk · Sleuth Kit    │
│  pwntools · ROPgadget│ Keystone (assembler)          │
└──────────────────────────────────────────────────────┘

Core Infrastructure (37 modules)

The reversecore_mcp/core/ directory contains the shared infrastructure that all tools build on:

Module	Purpose
`config.py`	Pydantic BaseSettings with 34+ environment variables
`security.py`	Input sanitization, command argument validation
`validators.py`	File and binary path validation with TOCTOU mitigation, symlink resolution
`r2_pool.py`	Thread-safe Radare2 connection pool with configurable size
`r2_helpers.py`	Structured Radare2 output parsing
`metrics.py`	Per-tool execution times, call counts, error rates, cache statistics
`memory.py`	Async SQLite-backed AI memory store for persisting analysis findings across sessions
`mitre_mapper.py`	MITRE ATT&CK technique ID mapping engine
`evidence.py`	Evidence classification system: `OBSERVED`, `INFERRED`, `POSSIBLE`
`resilience.py`	Retry, circuit-breaker, and timeout decorator patterns
`task_queue.py`	Background task queue via Redis + arq
`extension_registry.py`	Plugin registration and lifecycle management
`arch_registry.py`	Multi-architecture mapping (x86, x86_64, ARM32, ARM64, MIPS, RISC-V, PPC → r2 arch/bits/registers)
`result_cache.py`	SHA256-based tool result caching decorator (`@cache_tool_result`)
`analysis_cache.py`	Multi-level decompilation cache (L1: Redis, L2: SQLite)
`result.py`	`ToolSuccess` / `ToolError` Pydantic models
`exceptions.py`	17 exception classes with `RCMCP-E*` error codes
`decorators.py`	`@log_execution`, `@track_metrics`
`error_handling.py`	`@handle_tool_errors` decorator
`error_formatting.py`	Structured error response formatting
`execution.py`	Safe subprocess execution with timeout and output limits
`command_spec.py`	Command specification for subprocess calls
`loader.py`	Dynamic tool module loader
`plugin.py`	Plugin base class
`extension.py`	Extension base class
`container.py`	Container/sandbox execution support
`audit.py`	Audit logging
`binary_cache.py`	Binary file caching
`json_utils.py`	JSON serialization via orjson (3-5x faster than stdlib json)
`logging_config.py`	Loguru-based structured logging
`report_generator.py`	Report rendering engine (Markdown, PDF via xhtml2pdf)
`resource_manager.py`	MCP resource lifecycle management
`sast/python_ast_scanner.py`	Python AST-based vulnerability scanner
`sast/regex_scanner.py`	C/C++ regex-based vulnerability scanner
`sast/rule_manager.py`	SAST rule loading and management

Tool Catalog (151 Tools)

📖 Authoritative Tool Catalog & Parameter Reference (151 Tools) 📊 Tool Profile Footprint & Context Benchmark Matrix

Every tool returns a structured ToolResult — either a ToolSuccess with typed data or a ToolError with an RCMCP-E* error code. Tools are organized into 11 domain plugins across 5 modular execution profiles (full, static, malware, forensics, vuln-research):

Profile	Registered Tools	Schema Size	LLM Context Saving vs Full	Recommended Domain
`full`	151	112.6 KB	Baseline (0%)	Complete enterprise reverse engineering suite
`vuln-research`	103	70.2 KB	-37.7% (~10,800 tokens)	Fuzzing, ASan crash triage, CVE hunting, exploit research
`static`	97	64.8 KB	-42.5% (~12,200 tokens)	Disassembly, decompilation (r2ghidra), LIEF, static triage
`malware`	65	59.4 KB	-47.2% (~13,600 tokens)	Threat triage, YARA scanning, deobfuscation, anti-analysis
`forensics`	57	36.4 KB	-67.7% (~19,500 tokens)	Memory forensics (Volatility3), network PCAP (Scapy), disk

🔍 Static Analysis Plugin (24 tools)

#	Tool	Backend	Description
1	`run_strings`	`strings` CLI	ASCII/Unicode string extraction with configurable min-length
2	`run_binwalk`	Binwalk	Firmware deep-scan for embedded signatures and filesystems
3	`run_binwalk_extract`	Binwalk	Extract embedded files discovered by binwalk
4	`parse_binary_with_lief`	LIEF	Full PE/ELF/Mach-O header, section, import/export, TLS parsing
5	`detect_packer`	DIE	Quick packer/compiler detection
6	`detect_packer_deep`	DIE (`diec`)	Deep packer/protector analysis via Detect It Easy
7	`run_capa`	CAPA (Mandiant FLARE)	Capability detection — "encrypts data", "creates persistence", etc.
8	`run_capa_quick`	CAPA	Quick capability scan with a rule subset
9	`generate_signature`	Radare2	Generate binary signatures for identification
10	`generate_yara_rule`	Radare2 + YARA	Generate YARA detection rules from binary patterns
11	`generate_advanced_yara_rule`	Radare2 + YARA	Advanced YARA rules with behavioral indicators
12	`scan_for_versions`	LIEF + strings	Scan binary for embedded version strings
13	`extract_rtti_info`	Radare2	Extract C++ RTTI (Run-Time Type Information)
14	`diff_binaries`	Radare2	Semantic binary diff between two file versions
15	`analyze_variant_changes`	Radare2	Analyze changes between binary variants
16	`match_libraries`	Radare2	Identify statically linked libraries by function fingerprint
17	`patch_diff_1day`	Radare2 + heuristics	Automated patch diff analysis for 1-day vulnerability research
18	`analyze_patch_diff_auto`	Radare2 + inference	Automated patch vulnerability inference
19	`emulate_binary`	Radare2 ESIL	Register/memory-traced code emulation
20	`generate_fuzzing_harness`	Qiling + AFL++	Generate a fuzzing harness targeting a specific function
21	`run_fuzzing_campaign`	AFL++	Run a full fuzzing campaign with crash collection
22	`triage_crash`	GDB	Crash parsing and exploitability assessment
23	`verify_path_and_get_args`	angr	Symbolic execution — prove path reachability and compute concrete inputs
24	`taint_trace`	Radare2 + angr	Data-flow taint analysis from sources to sinks

🔐 Source Code Audit Plugin (1 tool)

#	Tool	Backend	Description
25	`audit_source_code`	AST + Regex	Python AST scanning + C/C++ regex scanning for dangerous patterns

🛠️ Common Utilities Plugin (20 tools)

File Operations (5 tools)

#	Tool	Description
26	`run_file`	File type, architecture, and compiler fingerprinting
27	`copy_to_workspace`	Copy a file into the analysis workspace
28	`create_directory`	Create a directory in the workspace
29	`list_workspace`	List all files in the workspace
30	`scan_workspace`	Full workspace scan with file metadata

Patch Explanation (1 tool)

#	Tool	Description
31	`explain_patch`	Explain a binary patch in natural language

Assembler (1 tool)

#	Tool	Backend	Description
32	`assemble_instructions`	Keystone	Assemble instructions to machine code (x86, ARM, MIPS, etc.)

AI Memory Management (11 tools)

These tools let the AI persist and recall findings across analysis sessions using an async SQLite database:

#	Tool	Description
33	`create_memory_session`	Start a new memory session for an analysis
34	`store_analysis_finding`	Persist an analysis finding with tags
35	`query_analysis_memories`	Search past findings by query
36	`get_binary_analysis_context`	Retrieve all context for a specific binary
37	`tag_analysis_session`	Add tags to a session for organization
38	`search_memories_by_tag`	Find sessions/findings by tag
39	`delete_analysis_session`	Remove a session and its findings
40	`cleanup_expired_sessions`	Remove sessions older than a threshold
41	`list_analysis_sessions`	List all active sessions
42	`export_memory_store`	Export all memories to a portable format
43	`import_memory_store`	Import memories from an export file

Server Monitoring (2 tools)

#	Tool	Description
44	`get_server_health`	Uptime, memory usage, loaded tools, Python version
45	`get_tool_metrics`	Per-tool call counts, mean execution times, error rates, cache hit/miss

⚙️ Radare2 & r2ghidra Plugin (30 tools)

All Radare2 tools use a thread-safe connection pool (r2_pool.py) that automatically manages r2pipe sessions.

#	Tool	Description
46	`Radare2_open_file`	Open a binary file in Radare2
47	`Radare2_close_file`	Close a Radare2 session
48	`Radare2_list_open_files`	List currently open files
49	`Radare2_analyze_binary`	Run full auto-analysis (`aaa`)
50	`Radare2_list_functions`	List all detected functions
51	`Radare2_disassemble_function`	Disassemble a specific function
52	`Radare2_disassemble_address`	Disassemble at a specific address
53	`Radare2_decompile_function`	Decompile via r2ghidra (Ghidra engine embedded in r2, no JVM needed)
54	`Radare2_list_exports`	List exported symbols
55	`Radare2_list_imports`	List imported functions
56	`Radare2_list_sections`	List binary sections with entropy
57	`Radare2_list_strings`	List strings found in the binary
58	`Radare2_find_cross_references`	Track function calls and data references
59	`Radare2_search_bytes`	Search for byte patterns in the binary
60	`Radare2_get_binary_info`	Get binary metadata (arch, format, endianness)
61	`Radare2_execute_command`	Execute a raw Radare2 command
62	`Radare2_esil_emulate`	ESIL emulation at a specific address
63	`Radare2_get_hexdump`	Hex dump at a virtual address
64	`Radare2_get_cfg_data`	Extract control flow graph data
65	`Radare2_generate_cfg_png`	Generate CFG as PNG image
66	`Radare2_generate_callgraph`	Generate function call graph
67	`Radare2_recover_structures`	Auto-recover C structs and persist to annotation database
68	`Radare2_decompile_with_r2ghidra`	High-quality C decompilation with caching
69	`Radare2_annotate_binary`	Add annotations to the binary
70	`Radare2_get_annotations`	Retrieve annotations
71	`Radare2_export_annotations`	Export annotations to file
72	`Radare2_import_annotations`	Import annotations from file
73	`Radare2_detect_crypto_constants`	Detect cryptographic constants (AES S-box, etc.)
74	`Radare2_find_gadgets`	Find ROP/JOP gadgets
75	`Radare2_calculate_entropy`	Calculate per-section entropy

🦠 Malware Analysis Plugin (9 tools)

#	Tool	Backend	Description
76	`dormant_detector`	Radare2 + heuristics	Find hidden backdoors, orphan functions, time-bombs, logic bombs
77	`adaptive_vaccine`	YARA + Radare2	Generate detection YARA rules + binary patches to neutralize threats
78	`vulnerability_hunter`	Radare2 + analysis	Detect dangerous API patterns (strcpy, sprintf) and ROP gadget chains
79	`extract_iocs`	Regex + LIEF	Extract IPs, URLs, domains, hashes, registry keys, crypto addresses
80	`run_yara`	YARA	Scan with custom rule files and built-in rulesets
81	`generate_poc_exploit`	pwntools	Generate proof-of-concept exploit code
82	`build_rop_chain`	ROPgadget + pwntools	Automated ROP chain construction
83	`autonomous_vuln_hunt`	Radare2 + angr	Autonomous vulnerability hunting pipeline
84	`analyze_heap_exploit`	Radare2 + heuristics	Heap exploitation analysis (UAF, double-free, overflow)

🕵️ Digital Forensics Plugin (22 tools)

Memory Forensics (6 tools)

#	Tool	Backend	Description
85	`memory_analyze`	Volatility3	Full memory dump analysis
86	`memory_list_processes`	Volatility3	List running processes from memory dump
87	`memory_detect_injections`	Volatility3	Detect code injection in process memory
88	`memory_extract_strings`	Volatility3	Extract strings from process memory
89	`memory_dump_module`	Volatility3	Dump a loaded module from memory
90	`memory_list_symbols`	Volatility3	List symbols from memory

Disk Forensics (6 tools)

#	Tool	Backend	Description
91	`disk_list_partition`	Sleuth Kit	List disk partitions
92	`disk_list_files`	Sleuth Kit	List files in a disk image
93	`disk_recover_deleted`	Sleuth Kit	Recover deleted files
94	`disk_analyze_mft`	Sleuth Kit	Analyze NTFS Master File Table
95	`disk_extract_file`	Sleuth Kit	Extract a file from disk image
96	`disk_hash_verify`	Sleuth Kit	Verify file integrity via hash

Network Forensics (5 tools)

#	Tool	Backend	Description
97	`pcap_analyze`	Scapy	PCAP analysis: protocol breakdown, anomalies
98	`pcap_list_connections`	Scapy	List all network connections
99	`pcap_extract_dns`	Scapy	Extract DNS queries and responses
100	`pcap_extract_c2`	Scapy	Identify potential C2 communication
101	`pcap_reconstruct_stream`	Scapy	Reconstruct TCP streams

Artifact Analysis (5 tools)

#	Tool	Backend	Description
102	`artifact_collect`	Custom parsers	Collect browser history, registry hives, event logs, prefetch
103	`artifact_correlate_ioc`	Custom parsers	Correlate artifacts with known IOCs
104	`artifact_generate_yara`	YARA	Generate YARA rules from artifact patterns
105	`artifact_timeline`	Custom parsers	Build timeline from multiple artifact sources
106	`artifact_report`	Custom parsers	Generate artifact analysis report

📝 Report Generation Plugin (14 tools)

#	Tool	Description
107	`get_system_time`	Get server timestamp (prevents AI from hallucinating dates)
108	`set_timezone`	Set the reporting timezone
109	`get_timezone_info`	Get current timezone information
110	`start_report_session`	Start a timed analysis session with unique ID
111	`end_report_session`	Finalize session: compute duration, lock IOC/ATT&CK lists
112	`get_report_session_status`	Check session status
113	`list_report_sessions`	List all active/completed sessions
114	`add_ioc`	Collect and tag IOCs during a live session
115	`add_analysis_note`	Add categorized notes (finding, warning, behavior)
116	`add_mitre_technique`	Document MITRE ATT&CK technique IDs
117	`set_severity`	Set session severity (low/medium/high/critical)
118	`create_analysis_report`	Render report in 4 modes: `full_analysis`, `quick_triage`, `ioc_summary`, `executive_brief`
119	`generate_vex_report`	Generate a VEX (Vulnerability Exploitability eXchange) report
120	`generate_sigma_rule`	Generate SIGMA detection rules

Guided Analysis Prompts (22 Modes)

Prompts are pre-built analysis workflows that prime the AI with a structured persona, step-by-step tool usage sequences, and evidence classification rules. You activate them by referencing the prompt name in your AI client.

Malware Analysis (9 prompts)

Prompt	Use Case
`full_analysis_mode`	6-phase comprehensive analysis: triage → disassembly → behavior → network → persistence → report
`malware_analysis_mode`	Focused malware analysis with threat classification
`basic_analysis_mode`	Rapid triage for initial assessment and quick verdicts
`apt_hunting_mode`	APT-specific hunting: lateral movement, persistence, data exfiltration
`malware_defense_mode`	Defense-oriented: generate detection rules and mitigations
`unpacking_mode`	Analyze and bypass packing/obfuscation (Themida, VMProtect, UPX)
`c2_extraction_mode`	Extract and analyze C2 communication infrastructure
`ransomware_triage_mode`	Ransomware-specific triage: encryption analysis, key recovery assessment
`code_similarity_mode`	Compare binaries for code similarity and shared lineage

Security Research (6 prompts)

Prompt	Use Case
`vulnerability_research_mode`	Bug hunting: buffer overflows, UAF, command injection
`crypto_analysis_mode`	Cryptographic implementation analysis and weakness detection
`firmware_analysis_mode`	IoT/embedded firmware: binwalk extraction, UART strings, hardcoded credentials
`patch_analysis_mode`	Security patch analysis and regression testing
`source_code_audit_mode`	Source code security audit (Python, C, C++)
`autonomous_vuln_hunt_mode`	Autonomous vulnerability hunting pipeline

CVE Research & Exploit Development (5 prompts)

Prompt	Use Case
`taint_analysis_mode`	Data-flow taint analysis: automated source→sink path discovery
`heap_exploit_mode`	Heap exploitation analysis and PoC generation
`fuzzing_mode`	Fuzzing campaign setup and crash triage
`patch_diff_auto_mode`	Automated patch diff for 1-day vulnerability research
`cve_discovery_pipeline_mode`	Full CVE discovery pipeline: from patch diff to working exploit

Other (2 prompts)

Prompt	Use Case
`game_analysis_mode`	Game client analysis: anti-cheat detection, protocol RE, memory inspection
`report_generation_mode`	Structured session workflow with MITRE ATT&CK technique mapping

How prompts work: Each prompt primes the AI with a structured analysis persona. It includes Chain-of-Thought reasoning checkpoints (where the AI must stop and evaluate before proceeding) and evidence classification rules that prevent the AI from stating speculation as fact. Every finding must be labeled as OBSERVED (directly verified), INFERRED (logically derived from static analysis), or POSSIBLE (requires further verification).

MCP Resources (11 URIs)

Resources are read-only data endpoints that AI clients can access through URI templates. They complement tools by providing structured data without requiring explicit tool calls.

Static Resources

URI	Description
`reversecore://guide`	Tool usage guide with file path rules and best practices
`reversecore://guide/structures`	Structure recovery and cross-reference analysis technical guide
`reversecore://tools`	Complete documentation for all 151 registered tools
`reversecore://logs`	Application logs (last 100 lines)

Dynamic Resources (Per-Binary Virtual Filesystem)

These URIs resolve per-binary and invoke the corresponding analysis tools on demand:

URI Template	Description
`reversecore://{filename}/strings`	Extract all strings from a binary
`reversecore://{filename}/iocs`	Extract IOCs (IPs, URLs, emails, hashes)
`reversecore://{filename}/func/{address}/code`	Decompiled pseudo-C code for a function
`reversecore://{filename}/func/{address}/asm`	Disassembly for a function
`reversecore://{filename}/func/{address}/cfg`	Control flow graph in Mermaid format
`reversecore://{filename}/functions`	List of all functions in the binary
`reversecore://{filename}/dormant_detector`	Dormant detector analysis results

Quick Start

Option 1 — PyPI (Simplest)

bash

pip install reversecore-mcp
reversecore-mcp

Prerequisites: Radare2 must be installed on your system (r2 --version). YARA is installed automatically via yara-python.

Option 2 — Docker (Recommended for Full Functionality)

All analysis engines (Radare2, r2ghidra, YARA, Binwalk, Sleuth Kit, GDB, etc.) come pre-installed:

bash

docker run -i --rm \
  -v /path/to/your/samples:/app/workspace \
  -e REVERSECORE_WORKSPACE=/app/workspace \
  -e MCP_TRANSPORT=stdio \
  ghcr.io/sjkim1127/reversecore_mcp:latest

Option 3 — Build from Source (Docker Compose)

bash

git clone https://github.com/sjkim1127/Reversecore_MCP.git
cd Reversecore_MCP
./scripts/run-docker.sh        # auto-detects Intel / Apple Silicon

Or manually:

bash

docker compose --profile x86 up -d    # Intel/AMD
docker compose --profile arm64 up -d  # Apple Silicon (M1/M2/M3)

Option 4 — Python (Local Development)

bash

git clone https://github.com/sjkim1127/Reversecore_MCP.git
cd Reversecore_MCP
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
python -m reversecore_mcp.server

Prerequisites for local mode: Radare2 must be installed on your system (r2 --version). Individual tool backends (YARA, LIEF, Capstone, etc.) are installed via pip. For full forensics support, you'll also need Volatility3, Scapy, and Sleuth Kit.

Connect to Your AI Client

Add the server configuration to your IDE client settings (e.g., ~/.cursor/mcp.json or claude_desktop_config.json).

⚡ Option 1: Docker Exec Mode (Recommended)

If you have the container running via Docker Compose, this mode channels stdio directly into the running container. Zero startup latency, persistent memory, and full tool availability.

json

{
  "mcpServers": {
    "Reversecore_MCP": {
      "command": "docker",
      "args": [
        "exec",
        "-i",
        "-e",
        "MCP_TRANSPORT=stdio",
        "reversecore-mcp-arm64",
        "python",
        "-m",
        "reversecore_mcp.server"
      ]
    }
  }
}

Replace reversecore-mcp-arm64 with reversecore-mcp if you are on Intel/AMD.

🌐 Option 2: SSE HTTP Mode

For network-based streaming (Server-Sent Events):

json

{
  "mcpServers": {
    "Reversecore_MCP": {
      "url": "http://localhost:8000/mcp/sse"
    }
  }
}

📦 Option 3: Stdio Mode (Docker-on-Demand)

Runs a fresh, isolated container for every session:

🍎 macOS

json

{
  "mcpServers": {
    "reversecore": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "/Users/YOUR_USERNAME/samples:/app/workspace",
        "-e", "REVERSECORE_WORKSPACE=/app/workspace",
        "-e", "MCP_TRANSPORT=stdio",
        "ghcr.io/sjkim1127/reversecore_mcp:latest"
      ]
    }
  }
}

🐧 Linux

json

{
  "mcpServers": {
    "reversecore": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "/home/YOUR_USERNAME/samples:/app/workspace",
        "-e", "REVERSECORE_WORKSPACE=/app/workspace",
        "-e", "MCP_TRANSPORT=stdio",
        "ghcr.io/sjkim1127/reversecore_mcp:latest"
      ]
    }
  }
}

🪟 Windows

json

{
  "mcpServers": {
    "reversecore": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "C:/samples:/app/workspace",
        "-e", "REVERSECORE_WORKSPACE=/app/workspace",
        "-e", "MCP_TRANSPORT=stdio",
        "ghcr.io/sjkim1127/reversecore_mcp:latest"
      ]
    }
  }
}

⚠️ Important — File Paths Inside Docker

Your local folder is mounted to /app/workspace inside the container. Always reference files by filename only, not by your local full path.

❌ Wrong ✅ Correct
r2_decompile("/Users/john/samples/mal.exe") r2_decompile("mal.exe")

Configuration

All settings can be provided via environment variables or a .env file (see .env.example). Settings are managed via Pydantic BaseSettings with the REVERSECORE_ prefix.

Core Settings

Variable	Default	Description
`MCP_TRANSPORT`	`stdio`	Transport mode: `stdio` or `http`
`REVERSECORE_WORKSPACE`	`./` (cwd)	Analysis workspace directory
`REVERSECORE_READ_DIRS`	`""`	Comma-separated list of additional read-only directories
`REVERSECORE_STRICT_PATHS`	`false`	Raise errors for missing paths instead of warnings
`REVERSECORE_STRUCTURED_ERRORS`	`false`	Enable structured error responses with error codes
`REVERSECORE_DEFAULT_TOOL_TIMEOUT`	`120`	Default tool execution timeout in seconds
`REVERSECORE_MAX_OUTPUT_SIZE`	`10000000`	Maximum output size for tools (bytes)

HTTP Mode Settings

Variable	Default	Description
`MCP_HOST`	`0.0.0.0`	Host interface to bind (auto-overrides to `127.0.0.1` if no API key)
`MCP_PORT`	`8000`	Port for HTTP server
`MCP_API_KEY`	(unset)	API key for HTTP authentication (`X-API-Key` or `Authorization: Bearer`)
`REVERSECORE_RATE_LIMIT`	`60`	Max requests per minute (HTTP mode only, via slowapi)
`MAX_UPLOAD_SIZE`	`100000000`	Maximum upload size (100 MB default)
`FILE_RETENTION_MINUTES`	`1440`	Retention period for uploaded files (24h default)

Radare2 Settings

Variable	Default	Description
`REVERSECORE_R2_POOL_SIZE`	`3`	Number of Radare2 connections in the pool
`REVERSECORE_R2_POOL_TIMEOUT`	`30`	Timeout for acquiring a connection from the pool
`REVERSECORE_R2_EXTENSIONS`	`""`	Comma-separated list of r2 extension classes (`module:ClassName`)
`REVERSECORE_GHIDRA_MAX_PROJECTS`	`3`	Max cached r2ghidra decompiler projects
`REVERSECORE_GHIDRA_EXTENSIONS`	`""`	Comma-separated list of Ghidra extension classes
`MAX_EMULATION_INSTRUCTIONS`	`1000`	Maximum ESIL emulation instructions

Sandbox Settings

Variable	Default	Description
`REVERSECORE_SANDBOX_ENABLED`	`false`	Enable sandbox execution for dynamic analysis tools
`REVERSECORE_SANDBOX_MODE`	`auto`	Sandbox mode: `auto`, `host`, `container`, `disabled`
`REVERSECORE_SANDBOX_DOCKER_IMAGE`	`reversecore-sandbox:latest`	Docker image for sandbox execution
`REVERSECORE_SANDBOX_CPU_LIMIT`	`1.0`	CPU core limit for sandbox containers
`REVERSECORE_SANDBOX_MEMORY_LIMIT`	`512m`	Memory limit for sandbox containers
`REVERSECORE_SANDBOX_PIDS_LIMIT`	`100`	PID limit for sandbox containers
`REVERSECORE_SANDBOX_USER`	`nobody`	Non-root user for sandbox execution

Storage & Queue

Variable	Default	Description
`REDIS_URL`	`redis://localhost:6379/0`	Redis URL for task queue and result caching
`MEMORY_DB_PATH`	`~/.reversecore_mcp/memory.db`	Path to AI memory SQLite database
`REVERSECORE_LIEF_MAX_FILE_SIZE`	`1000000000`	Maximum file size for LIEF parsing (1 GB)

Logging

Variable	Default	Description
`LOG_LEVEL`	`INFO`	Logging verbosity: `DEBUG`, `INFO`, `WARNING`, `ERROR`
`LOG_FILE`	`<tempdir>/reversecore/app.log`	Path to log file
`LOG_FORMAT`	`human`	Log format: `human` (readable) or `json` (structured)

Plugins & SAST

Variable	Default	Description
`REVERSECORE_PLUGIN_DIRS`	`""`	Comma-separated directories to scan for extension plugins
`REVERSECORE_SAST_RULES_PATH`	`""`	Path to custom YAML SAST rules file

Security Model

Security is implemented as defense-in-depth, with protections at multiple layers:

Input & Path Safety

Control	Implementation
No shell injection	All subprocess calls use list arguments, never shell strings (`execution.py`)
Path traversal prevention	`validate_file_path()` and `validate_binary_path()` resolve symlinks and confine access to the workspace (`validators.py`)
TOCTOU mitigation	`bypass_cache=True` flag re-validates paths to prevent race conditions
Input sanitization	All parameters sanitized before execution (`security.py`)
CSRF protection	Dashboard forms require token-based CSRF validation (`dashboard/__init__.py`)

Network & Authentication

Control	Implementation
Timing-attack-safe auth	`secrets.compare_digest()` for API key comparison (`web/auth.py`)
Restricted auth vectors	Only `X-API-Key` and `Authorization: Bearer` headers accepted; no query params or cookies
Loopback-only fallback	Without `MCP_API_KEY`, HTTP access restricted to `127.0.0.1` (`web/middleware.py`)
Rate limiting	Configurable per-minute limits via slowapi
Security headers	HSTS, X-Content-Type-Options, X-Frame-Options, CSP on all HTTP responses (`web/middleware.py`)
Minimized `/health`	Public endpoint returns only `{"status": "alive"}`; details behind authentication (`web/endpoints.py`)

Container & Runtime

Control	Implementation
Non-root execution	Runs as `appuser` (UID 1000) with minimal capabilities
Resource limits	Docker Compose enforces CPU (2.0) and memory (4 GB) limits
Sandbox isolation	Optional container-based sandboxing for dynamic analysis tools

CI/CD Security Gates

Control	Implementation
Secrets scanning	Gitleaks runs on every commit (pre-commit hook + CI)
SAST	Bandit scans all Python code on every commit
CodeQL	GitHub CodeQL static analysis on every push to `main`
Dependency auditing	pip-audit on every push — no unreviewed CVEs
Container scanning	Trivy scans Docker images for vulnerabilities (LOW through CRITICAL)
Exploit safety gate	POC templates scanned with Bandit; Hypothesis DAST fuzzing; container isolation verified

Structured Error Handling

All 17 exception classes carry RCMCP-E* error codes for programmatic handling. See Error Handling for the full hierarchy.

Development

Setup

bash

git clone https://github.com/sjkim1127/Reversecore_MCP.git
cd Reversecore_MCP
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
pip install -r requirements-dev.txt
pre-commit install   # installs Ruff, Bandit, Gitleaks hooks

Testing

bash

# Full test suite with coverage report
pytest tests/ -v

# Unit tests only (fast, no external dependencies)
pytest tests/unit/ -v

# Integration tests (requires Docker)
pytest tests/integration/ -v

# Run with coverage threshold enforcement
pytest tests/unit/ --cov=reversecore_mcp --cov-fail-under=80

# Run a specific test
pytest tests/unit/test_cli_tools.py::TestRunFile::test_success -v

# Security boundary tests
pytest tests/ -m security -v

# Benchmarks
pytest tests/ -m benchmark -v

Test status:

✅ 1,957 unit tests passing across Python 3.10 / 3.11 / 3.12
📊 87% code coverage (80% minimum enforced in CI)
🔒 Zero Bandit findings
⚡ Fully async test suite via pytest-asyncio

Test markers:

Marker	Purpose
`@pytest.mark.unit`	Fast unit tests
`@pytest.mark.integration`	Tests requiring Docker or external tools
`@pytest.mark.slow`	Long-running tests
`@pytest.mark.benchmark`	Performance benchmarks
`@pytest.mark.security`	Security boundary validation tests

Code Quality

bash

ruff check reversecore_mcp/      # Lint (E, W, F, I, B, C4, UP rules)
ruff format reversecore_mcp/     # Format
mypy reversecore_mcp/            # Type check (0 errors across 108 files)
bandit -r reversecore_mcp/       # Security scan (all severities)
pip-audit                        # Dependency CVE scan

Pre-commit Hooks

The following hooks run automatically on every commit:

Ruff — lint with auto-fix + format check
trailing-whitespace — remove trailing whitespace
end-of-file-fixer — ensure files end with newline
check-yaml / check-json — validate YAML/JSON syntax
check-added-large-files — block files > 1 MB
check-merge-conflict — detect unresolved merge markers
detect-private-key — prevent accidental key commits
Bandit — Python security scanning

CI/CD Pipeline

Every push to main triggers 11 pipeline jobs. All must pass before deployment.

code

 Lint & Security Gate              Unit Tests (Python Matrix)
   ├─ Gitleaks (secret scan)         ├─ pytest 3.10 --cov-fail-under=80
   ├─ Hadolint (Dockerfile lint)     ├─ pytest 3.11 --cov-fail-under=80
   ├─ Ruff check + format            └─ pytest 3.12 --cov-fail-under=80
   ├─ Mypy type check (108 files)
   ├─ Bandit (all severities)      Wheel Smoke Test
   ├─ pip-audit (no CVEs)            └─ Build wheel → install in /tmp
   └─ Security boundary tests            → verify plugin discovery
                                          → assert __file__ under sys.prefix
 CodeQL Analysis
   └─ Python SAST                  Docker Verification
                                     ├─ Build reversecore-mcp:ci
 Exploit Safety Gate                 ├─ Trivy container scan
   ├─ Bandit on POC templates        ├─ Image size check (< 5 GB)
   ├─ Hypothesis DAST fuzzing        ├─ CLI tool verification
   ├─ Performance benchmarks         ├─ Integration tests in container
   └─ Container isolation test       └─ E2E tool invocation

 In-Container Smoke Test           Build Base Image (amd64 + arm64)
   ├─ Copy test ELF into container   ├─ Compile YARA 4.3.1
   └─ Run scripts/smoke_test.py     ├─ Compile Radare2 6.0.4
                                     ├─ Compile r2ghidra
 Deploy (amd64 + arm64)             └─ Push to GHCR
   ├─ Build app image
   ├─ Push to GHCR                 Merge Manifests
   └─ Trivy rescan on published     └─ Multi-arch manifest → :latest

Zero-bypass policy: CI/CD failures are never resolved by modifying pipeline configuration. Root causes are always fixed directly in source code or dependencies.

Docker Build Architecture

The Docker build uses a two-layer approach to keep build times manageable:

Layer 1: Base Image (`Dockerfile.base`)

A multi-stage build that compiles all slow-to-build, rarely-changing dependencies from source:

code

compiler-toolchain (python:3.12-slim-bookworm + build tools)
    ├── compiler-yara      (YARA 4.3.1 from source)     [parallel]
    ├── compiler-r2        (Radare2 6.0.4 from source)   [parallel]
    │     └── compiler-r2ghidra  (r2ghidra plugin)       [sequential]
    └── compiler-pip       (pip install into /opt/venv)  [parallel]

base (final runtime: python:3.12-slim-bookworm)
    ├── Runtime packages: file, binutils, gdb, binwalk, graphviz, nasm, sleuthkit
    ├── /opt/yara (compiled YARA)
    ├── /opt/radare2 (compiled r2 + r2ghidra)
    ├── /opt/venv (Python packages)
    └── Non-root user: appuser (UID 1000)

This image is rebuilt only when tool versions change. Build time: ~12 minutes.

Layer 2: Application Image (`Dockerfile`)

Inherits from the base image and copies application code:

code

FROM base image
    ├── COPY reversecore_mcp/ (application code)
    ├── COPY scripts/ (smoke test, benchmarks)
    ├── pip install any new requirements
    ├── Security package upgrades
    └── CMD ["python", "-m", "reversecore_mcp.server"]

Build time: ~60 seconds.

Docker Compose

Three services with architecture-specific profiles:

Service	Profile	Description
`reversecore-mcp`	`default`, `x86`	Intel/AMD x86_64
`reversecore-mcp-arm64`	`arm64`, `macos`	Apple Silicon ARM64
`redis`	all profiles	Redis 7 Alpine for task queue and caching

Resource limits: 2.0 CPU cores, 4 GB memory per container.

System Requirements

Component	Minimum	Recommended
CPU	4 cores	8+ cores
RAM	8 GB	16 GB
Storage	20 GB	50 GB SSD
OS	Linux / macOS	Docker environment (any OS)
Docker	20.10+	24.0+
Python (local mode)	3.10	3.11 or 3.12

Project Structure

code

reversecore_mcp/
├── core/                          # Infrastructure layer (37 modules)
│   ├── config.py                  # Pydantic BaseSettings (34+ env vars)
│   ├── exceptions.py              # Exception hierarchy (17 classes, RCMCP-E* codes)
│   ├── security.py                # Input sanitization & command arg validation
│   ├── validators.py              # Path validators (TOCTOU-hardened, symlink-safe)
│   ├── r2_pool.py                 # Thread-safe Radare2 connection pool
│   ├── r2_helpers.py              # Structured Radare2 output parsing
│   ├── metrics.py                 # Per-tool timing, counts, error rates, cache stats
│   ├── decorators.py              # @log_execution, @track_metrics
│   ├── error_handling.py          # @handle_tool_errors decorator
│   ├── error_formatting.py        # Structured error formatting
│   ├── execution.py               # Safe subprocess with timeout/output limits
│   ├── command_spec.py            # Command specifications
│   ├── memory.py                  # Async SQLite AI memory store
│   ├── mitre_mapper.py            # MITRE ATT&CK mapping engine
│   ├── evidence.py                # Evidence classification (OBSERVED/INFERRED/POSSIBLE)
│   ├── resilience.py              # Retry, circuit-breaker, timeout patterns
│   ├── task_queue.py              # Background task queue (Redis + arq)
│   ├── extension_registry.py      # Plugin registration system
│   ├── arch_registry.py           # Multi-arch mapping (x86/ARM/MIPS/RISC-V/PPC)
│   ├── result_cache.py            # SHA256-based tool result caching
│   ├── analysis_cache.py          # Multi-level decompilation cache (Redis + SQLite)
│   ├── result.py                  # ToolSuccess / ToolError Pydantic models
│   ├── loader.py                  # Dynamic tool module loader
│   ├── plugin.py                  # Plugin base class
│   ├── extension.py               # Extension base class
│   ├── container.py               # Container/sandbox execution
│   ├── audit.py                   # Audit logging
│   ├── binary_cache.py            # Binary file caching
│   ├── json_utils.py              # orjson-backed JSON (3-5x faster)
│   ├── logging_config.py          # Loguru logging configuration
│   ├── report_generator.py        # Report rendering (Markdown, PDF)
│   ├── resource_manager.py        # MCP resource lifecycle
│   └── sast/                      # Source code scanners
│       ├── python_ast_scanner.py  # Python AST vulnerability scanner
│       ├── regex_scanner.py       # C/C++ regex vulnerability scanner
│       ├── rule_manager.py        # SAST rule loader
│       └── default_rules.yaml     # Default scanning rules
│
├── tools/                         # MCP tool implementations (151 tools)
│   ├── analysis/                  # Static analysis (24 tools)
│   │   ├── static_analysis.py     # file, strings, binwalk
│   │   ├── lief_tools.py          # LIEF binary parser
│   │   ├── capa_tools.py          # CAPA capability detection
│   │   ├── die_tools.py           # Detect It Easy packer detection
│   │   ├── diff_tools.py          # Binary diffing
│   │   ├── emulation_tools.py     # ESIL emulation
│   │   ├── fuzz_tools.py          # Fuzzing harness generator
│   │   ├── fuzzing_campaign.py    # Full fuzzing campaign runner
│   │   ├── symbolic_analysis.py   # angr symbolic execution
│   │   ├── signature_tools.py     # Library signature matching
│   │   ├── source_auditor.py      # SAST (Python + C/C++)
│   │   ├── crash_triage.py        # GDB crash triage
│   │   ├── taint_analysis.py      # Source→sink taint tracing
│   │   ├── advanced_yara.py       # Advanced YARA generation
│   │   ├── patch_vuln_inference.py # Patch vulnerability inference
│   │   └── cache_tools.py         # Analysis cache management
│   │
│   ├── radare2/                   # Disassembly & decompilation (30 tools)
│   │   ├── radare2_mcp_tools.py   # Core Radare2 tool set
│   │   ├── r2ghidra_tools.py      # r2ghidra decompiler (cached)
│   │   ├── r2_analysis.py         # Deep function analysis
│   │   ├── r2_db.py               # SQLite annotation + cache DB
│   │   ├── r2_esil_simulator.py   # Multi-arch ESIL simulator
│   │   └── r2_session.py          # Stateful analysis sessions
│   │
│   ├── malware/                   # Threat detection (9 tools)
│   │   ├── dormant_detector.py    # Backdoor/logic bomb detection
│   │   ├── ioc_tools.py           # IOC extraction
│   │   ├── yara_tools.py          # YARA scanning
│   │   ├── adaptive_vaccine.py    # YARA rule + patch generation
│   │   ├── vulnerability_hunter.py # Dangerous API detection
│   │   ├── autonomous_hunter.py   # Autonomous vuln hunting pipeline
│   │   ├── heap_exploit.py        # Heap exploitation analysis
│   │   ├── poc_generator.py       # PoC exploit generation
│   │   └── rop_builder.py         # ROP chain construction
│   │
│   ├── forensics/                 # Digital forensics (22 tools)
│   │   ├── memory.py              # Volatility3 memory forensics
│   │   ├── network.py             # Scapy PCAP analysis
│   │   ├── disk.py                # Sleuth Kit disk forensics
│   │   └── artifact.py            # Browser/registry/event log analysis
│   │
│   ├── report/                    # Report generation (14 tools)
│   │   ├── report_mcp_tools.py    # MCP-registered report tools
│   │   ├── report_tools.py        # Report rendering logic
│   │   ├── session.py             # Session state management
│   │   ├── converter.py           # Format conversion (Markdown → PDF/HTML)
│   │   ├── email.py               # SMTP report delivery
│   │   ├── sigma_generator.py     # SIGMA rule generation
│   │   └── vex_generator.py       # VEX report generation
│   │
│   └── common/                    # Shared utilities (20 tools)
│       ├── file_operations.py     # File ops, workspace management
│       ├── server_tools.py        # Server health, tool metrics
│       ├── memory_tools.py        # AI memory management (11 tools)
│       ├── patch_explainer.py     # Binary patch explanation
│       └── assembler.py           # Keystone assembler
│
├── prompts/                       # AI reasoning prompts (22 modes)
│   ├── malware.py                 # 9 malware analysis prompts
│   ├── security.py                # 6 security research prompts
│   ├── cve_research.py            # 5 CVE/exploit research prompts
│   ├── game.py                    # Game client analysis prompt
│   ├── report.py                  # Report generation prompt
│   ├── server_health.py           # Server inspection prompts
│   └── common.py                  # Shared constants (DOCKER_PATH_RULE, LANGUAGE_RULE)
│
├── dashboard/                     # Web dashboard (FastAPI + HTMX)
│   ├── templates/                 # Jinja2 templates with HTMX fragments
│   └── static/                    # htmx.min.js (local, CSP-compliant)
│
├── web/                           # HTTP transport layer
│   ├── auth.py                    # API key authentication middleware
│   ├── middleware.py              # Security headers, loopback restriction
│   └── endpoints.py               # /health, file upload, dashboard routes
│
├── resources.py                   # 11 MCP resources (static + dynamic per-binary)
└── server.py                      # FastMCP server entry point

Other directories:

code

tests/
├── unit/                          # 1,957 unit tests
├── integration/                   # Docker-based integration tests
├── fixtures/                      # Test binaries, YARA rules, sample data
└── conftest.py                    # Shared pytest fixtures

scripts/
├── smoke_test.py                  # Multi-layer in-container smoke test
├── check_release_metadata.py      # Version consistency validation
├── fetch_test_binaries.py         # Download test fixtures
├── run-docker.sh                  # Auto-detect architecture and start
└── ...                            # Benchmarks, analysis scripts

docs/
├── getting-started/               # Installation guide
├── development/                   # Architecture, contributing, testing guides
├── api/                           # Tool and module reference
└── user-guide/                    # Analysis workflows

Error Handling

All custom exceptions inherit from ReversecoreError and carry structured error codes:

Exception	Code	Type	When
`ReversecoreError`	`RCMCP-E000`	`UNKNOWN_ERROR`	Base class for all errors
`ValidationError`	`RCMCP-E001`	`VALIDATION_ERROR`	Invalid input, bad parameters
`ExecutionTimeoutError`	`RCMCP-E002`	`TIMEOUT_ERROR`	Tool exceeded timeout
`ToolNotFoundError`	`RCMCP-E003`	`TOOL_ERROR`	Required CLI tool not installed
`OutputLimitExceededError`	`RCMCP-E004`	`OUTPUT_ERROR`	Output exceeded max size
`ToolExecutionError`	`RCMCP-E005`	`EXECUTION_ERROR`	Subprocess returned non-zero
`BinaryAnalysisError`	`RCMCP-E100`	`BINARY_ANALYSIS_ERROR`	General binary analysis failure
`DecompilationError`	`RCMCP-E101`	`DECOMPILATION_ERROR`	r2ghidra decompilation failed
`DisassemblyError`	`RCMCP-E102`	`DISASSEMBLY_ERROR`	Radare2 disassembly failed
`StructureRecoveryError`	`RCMCP-E103`	`STRUCTURE_RECOVERY_ERROR`	C struct recovery failed
`SignatureGenerationError`	`RCMCP-E104`	`SIGNATURE_GENERATION_ERROR`	YARA/signature generation failed
`EmulationError`	`RCMCP-E105`	`EMULATION_ERROR`	ESIL emulation failed
`ToolTimeoutError`	`RCMCP-E200`	`TOOL_TIMEOUT_ERROR`	External tool timed out
`GhidraConnectionError`	`RCMCP-E201`	`GHIDRA_CONNECTION_ERROR`	r2ghidra connection issue
`Radare2Error`	`RCMCP-E202`	`RADARE2_ERROR`	Radare2 command failed
`WorkspaceError`	`RCMCP-E300`	`WORKSPACE_ERROR`	Workspace file access error
`SecurityViolationError`	`RCMCP-E301`	`SECURITY_VIOLATION`	Security policy violation
`PathTraversalError`	`RCMCP-E302`	`PATH_TRAVERSAL`	Path traversal attempt detected

AI clients can use the error_code field to programmatically handle failures and decide whether to retry, try an alternative tool, or report the error to the user.

Adding New Tools

Follow this pattern to add a new MCP tool:

python

# reversecore_mcp/tools/analysis/my_tool.py

from reversecore_mcp.core.decorators import log_execution
from reversecore_mcp.core.result import ToolResult, success, failure
from reversecore_mcp.core.security import validate_file_path


@log_execution()
async def my_analysis_tool(
    file_path: str,
    option: str | None = None,
) -> ToolResult:
    """Analyze a binary for X.

    Args:
        file_path: Path to the binary file (relative to workspace).
        option: Optional analysis option.

    Returns:
        ToolResult with status='success' and structured content.
    """
    try:
        safe_path = validate_file_path(file_path)
        result = await perform_analysis(safe_path)
        return success({"result": result})
    except Exception as e:
        return failure(
            error_code="RCMCP-E100",
            message=str(e),
            hint="Check that the file exists and is a valid binary.",
        )

Then register it in the appropriate plugin's __init__.py and add tests in tests/unit/.

Contributing

Fork the repository
Create a feature branch: git checkout -b feat/my-feature
Write tests alongside your code — coverage must not drop below 80%
Ensure all gates pass: pytest, ruff check, mypy, bandit
Open a pull request with a clear description

Please read the Contributing Guide for code standards, docstring conventions (Google-style), and the pull request checklist.

Documentation

Document	Description
Installation Guide	Detailed setup for all environments
Architecture Guide	System design & component details
Contributing Guide	Code standards, docstrings, PR workflow
Testing Guide	Test patterns, fixtures, and coverage
API Reference	Tool and module reference
User Guide	Analysis workflows

Usage Examples

Example 1: Basic Malware Triage

code

User: "Analyze this suspicious file sample.exe"

AI calls:
  1. run_file("sample.exe")           → PE32 executable, x86, MSVC
  2. detect_packer("sample.exe")      → Not packed
  3. extract_iocs("sample.exe")       → 3 IPs, 2 URLs, 1 mutex
  4. run_capa("sample.exe")           → "creates persistence", "encrypts data"
  5. dormant_detector("sample.exe")   → 2 orphan functions with network calls
  6. generate_yara_rule("sample.exe") → Detection rule generated

AI response: "This PE32 binary shows ransomware-like behavior. CAPA detected
encryption and persistence capabilities. I found 2 hidden network functions
that may serve as a backup C2 channel. Here's a YARA rule for detection..."

Example 2: Vulnerability Research with Taint Analysis

code

User: "Find exploitable bugs in this network daemon"

AI activates: taint_analysis_mode

AI calls:
  1. taint_trace("daemon", verify_with_angr=True)
     → Found 3 source→sink paths:
       recv() → strcpy()   [CWE-120, CONFIRMED by angr]
       read() → sprintf()  [CWE-134, LIKELY]
       getenv() → system() [CWE-78, POSSIBLE]

  2. vulnerability_hunter("daemon")
     → 12 dangerous API calls, 4 exploitable patterns

  3. generate_poc_exploit(target="daemon", vuln_type="bof", offset=128)
     → Python exploit script generated

AI response: "I found a confirmed stack buffer overflow where recv() data
flows directly into strcpy() at 0x40123C. angr proved the path is reachable.
Here's a working PoC..."

Example 3: Digital Forensics Investigation

code

User: "Analyze this memory dump from a compromised server"

AI calls:
  1. memory_list_processes("memdump.raw")
     → 47 processes, 2 with suspicious names

  2. memory_detect_injections("memdump.raw")
     → Code injection detected in PID 1842 (svchost.exe)

  3. memory_extract_strings("memdump.raw", pid=1842)
     → C2 domain strings extracted

  4. artifact_correlate_ioc(artifacts={"domains": ["evil-c2.com"]})
     → Matches known APT group IOCs

  5. create_analysis_report(template_type="full_analysis")
     → PDF report with timeline and MITRE ATT&CK mapping

Example 4: Patch Diffing for 1-day Research

code

User: "Compare the patched and unpatched versions to find what was fixed"

AI activates: patch_diff_auto_mode

AI calls:
  1. diff_binaries("libfoo-1.0.so", "libfoo-1.1.so")
     → 3 functions changed, 1 new function

  2. patch_diff_1day("libfoo-1.0.so", "libfoo-1.1.so")
     → Automated analysis: bounds check added at parse_header()

  3. r2_decompile("libfoo-1.0.so", "parse_header")
     → Decompiled vulnerable version (no bounds check)

  4. r2_decompile("libfoo-1.1.so", "parse_header")
     → Decompiled patched version (memcpy size limited)

AI response: "The patch adds a bounds check in parse_header() at 0x12340.
The old version copies user-controlled length bytes via memcpy without
validation, creating a heap buffer overflow (CWE-122)."

Multi-Architecture Support

The arch_registry.py module maps architecture names to Radare2 configuration parameters, enabling tools to work across different CPU architectures without manual configuration:

Architecture	Key	r2 Arch	Bit Widths	PC Register	SP Register
Intel 32-bit	`x86`	`x86`	32	`eip`	`esp`
Intel/AMD 64-bit	`x86_64`	`x86`	64	`rip`	`rsp`
ARM 32-bit / Thumb	`arm32`	`arm`	16, 32	`r15`	`r13`
ARM 64-bit (AArch64)	`arm64`	`arm`	64	`pc`	`sp`
MIPS	`mips`	`mips`	32, 64	`pc`	`sp`
RISC-V	`riscv`	`riscv`	32, 64	`pc`	`sp`
PowerPC	`ppc`	`ppc`	32, 64	`pc`	`r1`

Alias resolution is handled automatically:

amd64 → x86_64
aarch64 → arm64
arm with bits=64 → arm64
arm with bits=16 or bits=32 → arm32

Tools like Radare2_esil_emulate, assemble_instructions, and r2_simulate_patch use this registry to configure the analysis environment correctly for any target binary.

Result Cache System

Two caching layers minimize redundant computation:

Tool Result Cache (`result_cache.py`)

The @cache_tool_result decorator caches any tool's output based on a SHA256 hash of the binary file and the tool's keyword arguments:

code

Cache key = SHA256( "<tool_name>::{sorted_json_kwargs}" )

Storage backend: SQLite database via r2_db.py, accessible through get_cached_result() and set_cached_result() tools.

Metrics: Cache hits and misses are tracked via metrics_collector.record_cache_hit() and record_cache_miss(), visible through the get_tool_metrics tool.

Analysis Cache (`analysis_cache.py`)

A multi-level cache specifically for decompilation results (which are expensive to compute):

Level	Backend	Key Format	TTL	Purpose
L1	Redis	`ghidra:decompile:{file_hash}:{function_address}:{decompiler}`	1 hour (3600s)	Fast, shared across sessions
L2	SQLite	Table `decompilation_cache`	Persistent	Survives Redis restarts

Import/Export: The export_analysis_cache and import_analysis_cache tools allow saving cache state to/from rcpack files for sharing between environments.

AI Memory System

The AI memory system (memory_tools.py + core/memory.py) provides persistent, queryable storage for analysis findings across sessions. This allows the AI to:

Remember what it previously found about a binary
Cross-reference findings between different samples
Tag and search sessions by topic, malware family, or technique

How It Works

code

create_memory_session("analysis of ransomware sample")
    │
    ├── store_analysis_finding("Found AES-256 encryption at 0x401000", tags=["crypto", "ransomware"])
    ├── store_analysis_finding("C2 beacon interval: 30 seconds", tags=["c2", "network"])
    └── tag_analysis_session(tags=["ransomware", "financial-sector"])

# Later, in a different session:
query_analysis_memories("ransomware encryption")
    → Returns previous findings about ransomware encryption patterns

get_binary_analysis_context("sample.exe")
    → Returns all findings ever recorded for this binary

Storage: Async SQLite database at the path configured by MEMORY_DB_PATH (default: ~/.reversecore_mcp/memory.db).

Portability: Use export_memory_store and import_memory_store to transfer the entire memory database between environments.

Web Dashboard

When running in HTTP mode (MCP_TRANSPORT=http), a web dashboard is available at http://localhost:8000/dashboard. It provides:

Binary upload with drag-and-drop
Real-time analysis status
Interactive function list and disassembly view
IOC extraction results
Server health monitoring

Tech stack: FastAPI + Jinja2 templates + HTMX (loaded locally from dashboard/static/, no CDN dependency for CSP compliance).

Security features:

CSRF tokens on all state-changing forms
Jinja2 auto-escaping enabled
All user input sanitized via html.escape() before display
Path traversal protection via validate_file_path()

Deployment

Production Checklist

Before deploying to production:

Item	How
Set API key	`MCP_API_KEY=<strong-random-key>`
Use non-root user	Built-in: container runs as `appuser` (UID 1000)
Set resource limits	Default: 2 CPU / 4 GB RAM in `docker-compose.yml`
Enable structured logging	`LOG_FORMAT=json` for log aggregation
Configure Redis	`REDIS_URL=redis://<host>:6379/0` for task queue and caching
Set workspace path	`REVERSECORE_WORKSPACE=/path/to/isolated/directory`
Review rate limits	`REVERSECORE_RATE_LIMIT=60` (requests/min, adjust as needed)
Enable sandbox	`REVERSECORE_SANDBOX_ENABLED=true` for dynamic analysis isolation

Health Checks

The server provides HTTP health check endpoints for orchestration:

bash

# Liveness (always 200 if process is running)
curl http://localhost:8000/health/live

# Readiness (checks tool availability)
curl http://localhost:8000/health/ready

# Full health (requires API key if configured)
curl -H "X-API-Key: <key>" http://localhost:8000/health

These endpoints are exempted from API key authentication so load balancers and container orchestrators can probe them.

Container Healthcheck

The Docker image includes a built-in HEALTHCHECK instruction that verifies TCP connectivity to port 8000 every 30 seconds. Docker and Kubernetes will automatically restart unhealthy containers.

Troubleshooting

Common Issues

Tool returns RCMCP-E003: Tool not found

The required CLI tool is not installed in the environment.

Solution: If using Docker, verify the tool is in the base image:

bash

docker exec reversecore-mcp-arm64 which r2 yara binwalk tsk_recover gdb

If using local Python installation, install the missing tool:

bash

# macOS
brew install radare2 yara binwalk sleuthkit

# Ubuntu/Debian
apt install radare2 yara binwalk sleuthkit

Timeout error (RCMCP-E002 / RCMCP-E200)

Analysis exceeded the configured timeout.

Solution: Increase the timeout:

bash

export REVERSECORE_DEFAULT_TOOL_TIMEOUT=300  # 5 minutes

For large binaries (>100 MB), consider using quick-scan variants:

run_capa_quick instead of run_capa
detect_packer instead of detect_packer_deep

Path traversal error (RCMCP-E302)

You referenced a file outside the workspace directory.

Solution: Copy the file into the workspace first:

code

copy_to_workspace("/path/to/file.exe")

Or mount additional directories as read-only:

bash

export REVERSECORE_READ_DIRS=/opt/samples,/mnt/evidence

Docker container won't start on Apple Silicon

Make sure you're using the ARM64 profile:

bash

docker compose --profile arm64 up -d

Or use the auto-detection script:

bash

./scripts/run-docker.sh

Redis connection refused

The task queue requires a running Redis instance.

Solution: Start Redis alongside the main service:

bash

docker compose --profile arm64 up -d   # Starts both reversecore and redis

Or disable Redis-dependent features by not setting REDIS_URL.

r2ghidra decompilation produces empty output

This usually means the function wasn't analyzed first.

Solution: Run analysis before decompilation:

code

Radare2_analyze_binary("sample.exe")
Radare2_decompile_function("sample.exe", "main")

FAQ

Does this replace Ghidra or IDA Pro?

No. This project is a complement, not a replacement. It uses r2ghidra (the Ghidra decompiler engine embedded in Radare2) for decompilation. It does not provide a GUI, and it does not have the interactive analysis workflow of a full disassembler. Its purpose is to let AI assistants perform analysis tasks programmatically.

Is a separate Ghidra or JDK installation needed?

No. The r2ghidra plugin embeds the Ghidra decompiler engine directly inside Radare2. No JDK, no Ghidra installation, no Ghidra project files. Just r2 with the r2ghidra plugin compiled in.

What MCP clients are supported?

Any client that implements the Model Context Protocol specification. Tested with: Claude Desktop, Cursor, Windsurf, and Google Antigravity. The server supports both stdio and HTTP/SSE transports.

Can I analyze Windows PE files on Linux/macOS?

Yes. Static analysis (disassembly, decompilation, string extraction, IOC extraction, YARA scanning) works on any file format regardless of host OS. Dynamic analysis (emulation, fuzzing) may have limitations depending on the target architecture.

How safe is it to analyze malware with this tool?

The Docker container provides isolation: non-root user, no network by default in CI, resource limits. For live malware analysis, we recommend running in a dedicated VM or using the sandbox feature (REVERSECORE_SANDBOX_ENABLED=true). Static analysis tools (r2, YARA, strings) never execute the target binary.

What's the maximum file size?

Default limits:

Upload: 100 MB (MAX_UPLOAD_SIZE)
LIEF parsing: 1 GB (REVERSECORE_LIEF_MAX_FILE_SIZE)
Tool output: 10 MB (REVERSECORE_MAX_OUTPUT_SIZE)

All limits are configurable via environment variables.

Acknowledgments

This project is built on the work of many open-source projects:

Project	Role in Reversecore MCP
Radare2	Disassembly, emulation, binary analysis
r2ghidra	Ghidra decompiler engine for Radare2
FastMCP	MCP server framework
YARA	Pattern matching for malware detection
LIEF	Binary format parsing (PE, ELF, Mach-O)
CAPA	Mandiant FLARE capability detection
angr	Symbolic execution engine
Capstone	Disassembly framework
Keystone	Assembly framework
pwntools	Exploit development toolkit
ROPgadget	ROP gadget finder
Volatility3	Memory forensics framework
Scapy	Network packet analysis
Sleuth Kit	Disk forensics toolkit
Binwalk	Firmware analysis
Detect It Easy	Packer/compiler detection

License

MIT — see LICENSE for details.

GitHub · PyPI · FastMCP Docs · MCP Spec · Radare2 · YARA

Install

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "reversecore-mcp": {
      "command": "uvx",
      "args": [
        "reversecore-mcp"
      ]
    }
  }
}

Reversecore MCP (Model Context Protocol)

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

Topics

Reversecore MCP

Table of Contents

What is Reversecore MCP?

What it covers

Architecture

Core Infrastructure (37 modules)

Tool Catalog (151 Tools)

🔍 Static Analysis Plugin (24 tools)

🔐 Source Code Audit Plugin (1 tool)

🛠️ Common Utilities Plugin (20 tools)

⚙️ Radare2 & r2ghidra Plugin (30 tools)

🦠 Malware Analysis Plugin (9 tools)

🕵️ Digital Forensics Plugin (22 tools)

📝 Report Generation Plugin (14 tools)

Guided Analysis Prompts (22 Modes)

Malware Analysis (9 prompts)

Security Research (6 prompts)

CVE Research & Exploit Development (5 prompts)

Other (2 prompts)

MCP Resources (11 URIs)

Static Resources

Dynamic Resources (Per-Binary Virtual Filesystem)

Quick Start

Option 1 — PyPI (Simplest)

Option 2 — Docker (Recommended for Full Functionality)

Option 3 — Build from Source (Docker Compose)

Option 4 — Python (Local Development)

Connect to Your AI Client

⚡ Option 1: Docker Exec Mode (Recommended)

🌐 Option 2: SSE HTTP Mode

📦 Option 3: Stdio Mode (Docker-on-Demand)

Configuration

Core Settings

HTTP Mode Settings

Radare2 Settings

Sandbox Settings

Storage & Queue

Logging

Plugins & SAST

Security Model

Input & Path Safety

Network & Authentication

Container & Runtime

CI/CD Security Gates

Structured Error Handling

Development

Setup

Testing

Code Quality

Pre-commit Hooks

CI/CD Pipeline

Docker Build Architecture

Layer 1: Base Image (Dockerfile.base)

Layer 2: Application Image (Dockerfile)

Docker Compose

System Requirements

Project Structure

Error Handling

Adding New Tools

Contributing

Documentation

Usage Examples

Example 1: Basic Malware Triage

Example 2: Vulnerability Research with Taint Analysis

Example 3: Digital Forensics Investigation

Example 4: Patch Diffing for 1-day Research

Multi-Architecture Support

Result Cache System

Tool Result Cache (result_cache.py)

Analysis Cache (analysis_cache.py)

AI Memory System

How It Works

Web Dashboard

Deployment

Layer 1: Base Image (`Dockerfile.base`)

Layer 2: Application Image (`Dockerfile`)

Tool Result Cache (`result_cache.py`)

Analysis Cache (`analysis_cache.py`)