Server operations MCP: HTTP health checks, log analysis, deploy dry-runs, mail diagnostics.
io.github.ellmos-ai/ellmos-servercommander-mcp is an Alpha Model Context Protocol (MCP) server for local-first server operations. It focuses on deployment dry-runs, mail configuration status, access-log analysis, and resilient HTTP health checks.
🛠️ Key Features
HTTP health checks
Log analysis (access logs; includes Apache and Nginx log topics)
Deployment dry-runs
Mail diagnostics / mail configuration status
🚀 Use Cases
Pre-deployment validation via dry-runs
Diagnosing mail configuration issues
Inspecting server access logs to support troubleshooting
Alpha Model Context Protocol (MCP) server for local-first server operations: deployment dry-runs, mail configuration status, access-log analysis, and resilient HTTP health checks.
Discoverability & AI Search: Published on npm as ellmos-servercommander-mcp, cataloged for MCP ecosystems in server.json, glama.json, and smithery.yaml, and indexed for AI/LLM search in llms.txt.
ellmos-servercommander-mcp is an authoritative, local-first Model Context Protocol (MCP) server engineered specifically for AI coding assistants and autonomous agent platforms (Claude Code, Cursor, Codex, Antigravity, Gemini). It enables agents to safely diagnose server health, analyze web server access logs, inspect mail readiness, and build dry-run deployment plans without exposing production infrastructure to unverified, destructive mutations or arbitrary shell execution.
Every operation is governed by strict local-first and zero-elevation guarantees:
100% Local-First & Zero-Egress by Default: Diagnostic parsing and manifest hashing execute locally; zero telemetry and zero unverified outbound network requests.
Dry-Run & Staging First: Deployment operations calculate SHA-256 tree digests and check target profiles before any remote command is staged.
Unprivileged Execution (RunAsInvoker): Operates within standard unprivileged user space without requiring root or administrator elevation.
2. Visual Architecture & System Topology
The following diagram illustrates the decoupled layers of ServerCommander, from MCP host transport and Node.js process supervision to Python dispatching, operational engines, and local persistence sinks:
flowchart TD
subgraph HostLayer ["1. MCP Host & AI Client Layer"]
Host["MCP Host: Claude Desktop / Claude Code / Cursor"]
end
subgraph GatewayLayer ["2. Gateway & Process Supervision Layer"]
NodeWrapper["Node.js CLI Wrapper (bin/ellmos-servercommander.js)"]
end
subgraph CoreLayer ["3. Python MCP Server Core Layer"]
FastMCP["Python MCP Server (FastMCP Transport stdio)"]
Dispatcher["Tool Dispatcher & Parameter Validator"]
i18nEngine["i18n Translation Engine (en, de, es, zh, ja, ru)"]
end
subgraph OperationsLayer ["4. Operations & Diagnostics Engines"]
HTTPProbe["HTTP Health Probe (sc_health_check)"]
LogAnalyzer["Apache/Nginx Log Analyzer (sc_logs_analyze)"]
DeployStaging["Deployment Staging & Manifest Planner (sc_deploy / sc_deploy_status)"]
MailDiagnostics["IMAP/SMTP Safety Diagnostics (sc_mail_*)"]
end
subgraph SinkLayer ["5. Local Storage & Audit Sink Layer"]
SQLiteHist[("Local SQLite Deploy History (deploy-history.db)")]
JSONReports[("Sanitized JSON Log Reports")]
AuditSink["Local Diagnostic Outputs & Stdout Stream"]
end
Host <-->|"stdio / JSON-RPC"| NodeWrapper
NodeWrapper <-->|"Child Process Stdio"| FastMCP
FastMCP --> Dispatcher
Dispatcher <--> i18nEngine
Dispatcher --> HTTPProbe
Dispatcher --> LogAnalyzer
Dispatcher --> DeployStaging
Dispatcher --> MailDiagnostics
DeployStaging -.->|"Optional opt-in persist"| SQLiteHist
LogAnalyzer -.->|"Optional persist_report"| JSONReports
HTTPProbe -.-> AuditSink
MailDiagnostics -.-> AuditSink
The following sequence diagram demonstrates the lifecycle of operations dispatched by an AI agent through ServerCommander, showing concurrent HTTP probing, log parsing, and dry-run manifest calculation:
sequenceDiagram
autonumber
actor User as AI Assistant / User
participant Host as MCP Host (Claude / Cursor)
participant Wrapper as Node.js Wrapper
participant Server as ServerCommander Server
participant Handler as Operation Handler
participant Disk as Local Disk / SQLite Sink
participant Target as Network Endpoint
User->>Host: "Check API health and prepare deploy manifest"
Host->>Wrapper: JSON-RPC request (stdio)
Wrapper->>Server: Forward request via child process
Server->>Server: Parse parameters & validate config
alt HTTP Health Probe
Server->>Handler: Dispatch sc_health_check
Handler->>Target: HTTP/HTTPS GET (async worker thread)
Target-->>Handler: Status code + Latency response
Handler-->>Server: Health result dictionary
else Access Log Analysis
Server->>Handler: Dispatch sc_logs_analyze
Handler->>Disk: Read access.log & parse entries
Handler->>Disk: Optional write structured JSON report
Handler-->>Server: Aggregated log statistics
else Deployment Staging
Server->>Handler: Dispatch sc_deploy (dry_run=True)
Handler->>Disk: Scan local_path & calculate SHA-256 tree
Handler->>Disk: Optional insert record into deploy-history.db
Handler-->>Server: Manifest digest & profile readiness
end
Server->>Server: Localize response messages (i18n engine)
Server-->>Wrapper: JSON-RPC response
Wrapper-->>Host: Formatted stdio output
Host-->>User: Structured operations summary & next steps
4. Target Personas & High-Intent SEO Queries
ServerCommander MCP bridges the critical gap between hazardous raw shell execution and opaque hosting control panels. It equips AI agents with safe, structured diagnostic capabilities for system administration.
Target Personas
Persona ID
Target Persona
Key Challenges & Pain Points
ServerCommander MCP Solution
[PERSONA-01]
Autonomous AI Agent Engineers & Tooling Architects
High risk of destructive bash commands during agent exploration
Structured JSON-RPC MCP tools with strict non-destructive defaults
[PERSONA-02]
DevOps & Site Reliability Engineers (SREs)
Undetected file drifts, broken releases, and unsafe sync operations
Deterministic SHA-256 tree hashing and local dry-run deployment plans
[PERSONA-03]
Security-Conscious System Administrators & SecOps
Credential leakage, root elevation risks, and suspicious traffic bursts
Tedious manual health monitoring and repetitive log grepping
Instant HTTP health checks and automated bot/error analysis from IDE
High-Intent Search & SEO Queries
"mcp server operations tools"
"mcp deploy dry-run server"
"mcp access log analyzer"
"mcp http health check tool"
"local-first server management mcp"
"claude code server operations mcp"
"safe sftp deployment planning mcp"
"ai assistant server preflight checks"
"apache nginx log analysis mcp"
"resilient http health check mcp"
"sqlite deploy history mcp"
5. Comparative Matrix vs. Alternatives
The 10-dimension matrix below contrasts ServerCommander against common server administration approaches, mapped directly to its runtime and governance invariants (INV-LOCAL-01 through INV-SLA-10):
Transport: Standard I/O (stdio) via the Python MCP SDK and Node.js process wrapper.
Package Status: Public alpha package under the ellmos-ai organization.
Current Core: MCP tool listing, tool dispatch, TOML configuration loader, HTTP health checks, richer access-log analysis with optional persisted JSON reports, and optional local dry-run deployment history.
Safe Alpha Handlers: sc_deploy builds local SHA-256 manifests, configuration diagnostics, and opt-in SQLite history records in dry-run mode; sc_mail_* reports protocol-specific IMAP/SMTP readiness without opening mail connections by default.
i18n Localization: Localized MCP tool descriptions, input-schema field descriptions, and unknown-tool errors for en, de, es, zh, ja, ru with automatic English fallback.
9. Installation & Prerequisites
The npm package contains a Node wrapper that starts the Python server. You still need Python 3.10+ and the Python package mcp>=1.0.0.
Avoid creating a .venv inside cloud-synced folders if your sync client locks files. If you need an isolated environment, create it outside that folder.
Secrets should always be referenced through environment variables, for example $MAIL_PASSWORD or $SFTP_PASSWORD.
12. Tools & Handlers Reference
sc_health_check: Checks HTTP/HTTPS endpoints and reports status codes, response headers, and latency. Malformed endpoint URLs are captured gracefully as failed checks rather than aborting the batch.
sc_logs_analyze: Analyzes Apache/Nginx access logs from inline text or local files, reporting HTTP status classes (2xx/3xx/4xx/5xx), total bytes transferred, top referrers, 404/500 error paths, suspicious bot markers, and optional JSON report persistence via persist_report.
sc_deploy: Creates a dry-run deployment plan with a local SHA-256 manifest and profile diagnostics without performing remote mutations. Nested symbolic links are tracked as skipped_symlinks to prevent unexpected directory traversal.
sc_deploy_status: Displays configured deployment profiles, profile diagnostics, and recent dry-run deployment records retrieved from the local SQLite history database.
sc_mail_list, sc_mail_read, sc_mail_send, sc_mail_search: Safe alpha status responses with action-specific IMAP/SMTP readiness diagnostics. With [mail].execution_enabled = true, sc_mail_list executes a read-only IMAP reachability probe (connect + folder listing) by reusing the canonical mail-connector module without reimplementing an IMAP client.
13. Search, Disambiguation & Discovery Keywords
ServerCommander is the ellmos operations MCP server for local-first server administration workflows. Use this repository when searching for:
MCP server operations tools
MCP deploy dry-run server
MCP access log analyzer
MCP HTTP health check tool
local-first server management MCP
Claude Code server operations MCP
safe SFTP deployment planning MCP
AI assistant server preflight checks
Apache Nginx log analysis MCP
resilient HTTP health check MCP
SQLite deploy history MCP
It is not the GitHub MCP server, not a generic arbitrary shell-execution MCP server, not a cloud hosting provider control panel, and not an unverified production SFTP/IMAP auto-executor. The current alpha surface is intentionally diagnostic, dry-run first, and safe by default.
14. Sibling Ecosystem Matrix
This MCP server is an integral component of the ellmos-ai ecosystem and the open-bricks open-source software family.
ellmos ServerCommander MCP is strictly built upon permissive open-source foundations. We maintain zero hidden telemetry, zero proprietary binary blobs, and zero unverified dynamic dependencies.
Direct Runtime: Python MCP SDK (mcp>=1.0.0, MIT License, Anthropic PBC), Python Standard Library (PSFL-2.0).
Audit Ledger & Level 1 SBOM: Comprehensive license disclosures, full copyright notices, and local-first compliance assurances are documented in THIRD_PARTY_LICENSES.md and plain-text companion THIRD_PARTY_LICENSES.txt. Formal repository attribution is preserved in NOTICE.
Dieses Open-Source-Softwareprodukt wird als unentgeltliche Schenkung im Sinne der §§ 516 ff. BGB bereitgestellt. Gemäß § 521 BGB ist die Haftung des Urhebers und der Beitragenden auf Vorsatz und grobe Fahrlässigkeit beschränkt. Ergänzend gelten die nachstehenden Haftungsausschlüsse der MIT-Lizenz.
Nutzung auf eigenes Risiko. Keine Wartungsverpflichtung, keine Verfügbarkeitszusicherung, keine Gewähr für Fehlerfreiheit oder Eignung für einen bestimmten Einsatzzweck.
English Summary
This project is an unpaid open-source donation. In accordance with § 521 of the German Civil Code (BGB), liability is restricted strictly to cases of intentional misconduct and gross negligence. Supplemental liability disclaimers are set forth in the MIT License below.
Use entirely at your own risk. No maintenance commitments, no availability guarantees, and no warranties regarding fitness for any particular purpose.
License & Attribution
Distributed under the terms of the MIT License.
Copyright (c) 2026 Lukas Geiger. See LICENSE and NOTICE for full details.
Third-party licenses and Level 1 SBOM notices are audited in THIRD_PARTY_LICENSES.md.