io.github.XJTLUmedia/context-first-mcp

Official

by XJTLUmedia · TypeScript

37-tool MCP server: context health, memory, reasoning, truthfulness & research pipeline.

Evaluate context health, memory, reasoning, and truthfulness in AI systems.

README.md18 KBView on GitHub

Context-First MCP

The MCP server that keeps your AI grounded, coherent, and honest — across every turn.

bash

npx context-first-mcp

Works instantly with Claude Desktop · Cursor · VS Code · any MCP client · Vercel remote — zero API keys needed.

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37 research-backed tools across 7 layers — context health, state, sandboxing, persistent memory, advanced reasoning, truthfulness verification, orchestration, structured research, and autonomous file export. One context_loop call replaces 6–7 individual tools and returns a unified action directive.

Why Your AI Conversations Break Down

Long AI conversations fail in predictable ways. Context-First fixes all four:

Failure Mode	What Goes Wrong	Context-First Solution
Context Drift	AI forgets earlier decisions and intent as the conversation grows	`context_loop` + `detect_drift` continuously re-anchor every turn
Silent Contradiction	New inputs silently overrule established facts — the AI doesn't notice	`detect_conflicts` compares every input against locked ground truth
Vague Execution	AI proceeds on underspecified requirements, producing misaligned output	`check_ambiguity` + `abstention_check` ask clarifying questions instead of guessing
Hallucinated Success	Tool outputs look successful but didn't actually achieve the goal	`verify_execution` rechecks whether the outcome matches the stated intent

What You Get

37 production-ready tools grouped into 7 layers — plus 1 orchestrator that runs them all:

code

context_loop  ─────────────────────────────────────────────────────────────────
  ├─ Layer 1 · Context Health   (9 tools)   recap, conflict, ambiguity, depth …
  ├─ Layer 2 · Sandbox          (3 tools)   discover_tools, quarantine, merge
  ├─ Layer 3 · Persistent Memory(6 tools)   store, recall, compact, graph …
  ├─ Layer 4 · Advanced Reasoning(5 tools)  InftyThink, Coconut, KAG, MindEvo …
  ├─ Layer 5 · Truthfulness     (7 tools)   NCB, IOE, verify_first, self_critique…
  └─ State + Research Pipeline + Export     (7 tools)

One call. One directive. One score.

json

{
  "directive": {
    "action": "clarify",
    "contextHealth": 0.62,
    "instruction": "Resolve with the user: (1) Is this a firm requirement? (2) Which framework?",
    "autoExtractedFacts": { "deploy_to": "Vercel" },
    "suggestedNextTools": ["verify_execution", "quarantine_context"]
  }
}

Quick Start

npx — zero install

bash

npx context-first-mcp

Claude Desktop

json

{
  "mcpServers": {
    "context-first": {
      "command": "npx",
      "args": ["-y", "context-first-mcp"]
    }
  }
}

Cursor / VS Code

json

{
  "mcp": {
    "servers": {
      "context-first": {
        "command": "npx",
        "args": ["-y", "context-first-mcp"]
      }
    }
  }
}

Remote (Streamable HTTP)

json

{
  "mcpServers": {
    "context-first": {
      "url": "https://context-first-mcp.vercel.app/api/mcp"
    }
  }
}

Deploy your own Vercel instance

Tool Reference

Layer 1: Core Context Health (9 tools)

Tool	Purpose
`context_loop`	One-call orchestrator. Runs 8 stages (ingest→recap→conflict→ambiguity→entropy→abstention→discovery→synthesis) and returns a single `directive` with `action`, `contextHealth` score, extracted facts, and suggested next tools
`recap_conversation`	Extracts hidden intent, key decisions, and produces consolidated state summaries
`detect_conflicts`	Compares new input against ground truth; surfaces contradictions
`check_ambiguity`	Identifies underspecified requirements and generates clarifying questions
`verify_execution`	Validates whether tool outputs actually achieved the stated goal
`entropy_monitor`	Proxy-entropy scoring via lexical diversity, contradiction density, hedge frequency, and n-gram repetition (ERGO)
`abstention_check`	5-dimension confidence scoring — abstains with questions rather than hallucinating (RLAAR)
`detect_drift`	Detects conversation drift from the original intent
`check_depth`	Evaluates response depth against question complexity

Layer 1b: State Management (4 tools)

Tool	Purpose
`get_state`	Retrieve confirmed facts and task status
`set_state`	Lock in ground truth — subsequent conflict checks run against these values
`clear_state`	Reset specific keys or all state
`get_history_summary`	Compressed conversation history with intent annotations

Layer 2: Sandbox & Discovery (3 tools)

Tool	Method	Purpose
`discover_tools`	MCP-Zero + ScaleMCP	Natural-language tool routing — returns only semantically relevant tools, reducing context bloat by up to 98%
`quarantine_context`	Multi-Agent Quarantine	Create isolated memory silos for sub-tasks, preventing intent dilution
`merge_quarantine`	Multi-Agent Quarantine	Merge silo results with noise filtering — only promoted keys return to main context

Layer 3: Persistent Memory (6 tools)

Tool	Purpose
`memory_store`	Store findings, decisions, and intermediate results with metadata
`memory_recall`	Retrieve relevant memories by semantic query
`memory_compact`	Compress and consolidate memory entries
`memory_graph`	Build and query a knowledge graph from stored memories
`memory_inspect`	Inspect memory store contents and statistics
`memory_curate`	Deduplicate and organize memory entries

Layer 4: Advanced Reasoning (5 tools)

Tool	Method	Purpose
`inftythink_reason`	InftyThink	Infinite-depth reasoning with adaptive stopping
`coconut_reason`	Coconut	Chain-of-Continuous-Thought in latent space
`extracot_compress`	ExtraCoT	Compress chain-of-thought while preserving reasoning fidelity
`mindevolution_solve`	MindEvolution	Evolutionary search over the solution space
`kagthinker_solve`	KAG-Thinker	Knowledge-augmented generation with structured thinking

Layer 5: Truthfulness & Verification (7 tools)

Tool	Purpose
`probe_internal_state`	Probe model consistency across paraphrased prompts
`detect_truth_direction`	Detect whether model reasoning is trending toward or away from truth
`ncb_check`	Neighborhood consistency check across semantically equivalent inputs
`check_logical_consistency`	Verify logical coherence of reasoning chains
`verify_first`	Pre-verification before committing to claims
`ioe_self_correct`	Intrinsic-extrinsic self-correction
`self_critique`	Structured self-critique with improvement suggestions

Research Pipeline & Export (2 tools)

Tool	Purpose
`research_pipeline`	Structured research orchestration across `init → gather → analyze → verify → finalize`. Covers all 34 underlying tool-equivalents — state, sandboxing, memory, reasoning, truthfulness, context health. Writes files autonomously to disk as the pipeline runs; no LLM cooperation needed for file output.
`export_research_files`	Writes every verified report chunk and/or every raw evidence batch to disk in a single call.

Built on Peer-Reviewed Research

Every core algorithm traces back to a published paper:

Algorithm	Paper	arXiv	Tool
MCP-Zero	Active Tool Request	2506.01056	`discover_tools`
ScaleMCP	Semantic Tool Grouping	2505.06416	`discover_tools` registry
ERGO	Entropy-based Quality	2510.14077	`entropy_monitor`
RLAAR	Calibrated Abstention	2510.18731	`abstention_check`

Implementation highlights:

Proxy Entropy (ERGO): 4 response-level proxy signals (lexical diversity, contradiction density, hedge-word frequency, n-gram repetition) replace inaccessible token-level logprobs. Composite score above threshold triggers adaptive context reset.
TF-IDF Discovery (MCP-Zero): Pure TypeScript, zero external dependencies. Indexes all tool descriptions at startup; cosine similarity routes queries to the top-k relevant tools only.
Inference-Time Abstention (RLAAR): 5-dimension confidence scoring replaces the RL training loop. Abstains with targeted questions when confidence < threshold — no hallucination fallback.

Export Helper (1 tool)

Tool	Description
`export_research_files`	Writes research artifacts directly to disk. It can automatically expand and write every verified report chunk without asking the LLM to loop `finalize` manually, and it can also write every gathered raw-evidence batch even when `verify` has not passed.

context_loop Pipeline

code

context_loop (single MCP tool call)
├── Stage 1: INGEST     — Store messages to session history
├── Stage 2: RECAP      — Extract intents, decisions, summaries
├── Stage 3: CONFLICT   — Detect contradictions against ground truth
├── Stage 4: AMBIGUITY  — Check for underspecified requirements
├── Stage 5: ENTROPY    — Monitor output quality degradation (ERGO)
├── Stage 6: ABSTENTION — Multi-dimensional confidence check (RLAAR)
├── Stage 7: DISCOVERY  — Suggest relevant next tools (MCP-Zero)
└── Stage 8: SYNTHESIS   — Combine signals → action recommendation + LLM directive

Synthesis Priority: abstain > reset > clarify > proceed

Each stage runs with independent error isolation — a failure in one stage doesn't block the others. The result includes per-stage timing, status, and detailed results for observability.

LLM Directive (NEW)

The context_loop response includes a top-level directive object designed for LLM consumption — a compact, actionable instruction that replaces the need to parse nested stage results:

json

{
  "directive": {
    "action": "clarify",
    "instruction": "Before proceeding, resolve these issues with the user:\n1. Could you specify exactly what you mean?\n2. Is this a firm requirement or still open for discussion?",
    "questions": ["Could you specify exactly what you mean?", "Is this a firm requirement?"],
    "contextHealth": 0.62,
    "autoExtractedFacts": { "framework": "React", "deploy_to": "Vercel" },
    "suggestedNextTools": ["verify_execution", "quarantine_context"]
  }
}

How `context_loop` Works

code

context_loop (single MCP tool call)
├── Stage 1: INGEST     — Store messages to session history
├── Stage 2: RECAP      — Extract intents, decisions, summaries
├── Stage 3: CONFLICT   — Detect contradictions against ground truth
├── Stage 4: AMBIGUITY  — Check for underspecified requirements
├── Stage 5: ENTROPY    — Monitor output quality degradation (ERGO)
├── Stage 6: ABSTENTION — Multi-dimensional confidence check (RLAAR)
├── Stage 7: DISCOVERY  — Suggest relevant next tools (MCP-Zero)
└── Stage 8: SYNTHESIS  — Combine signals → action + directive

Synthesis priority: abstain > reset > clarify > proceed

Each stage runs with independent error isolation. The directive response field carries everything an LLM needs:

Field	Description
`action`	`proceed` · `clarify` · `reset` · `abstain`
`instruction`	Plain-language guidance for the LLM's next step
`questions`	Aggregated clarifying questions (ambiguity + abstention + conflicts)
`contextHealth`	0–1 composite score. 1 = healthy, 0 = degraded
`autoExtractedFacts`	Key-value facts auto-extracted from user messages and stored as ground truth
`suggestedNextTools`	Relevant tools the LLM should consider next

Smart defaults: currentInput is auto-inferred from the last user message. Facts like "use React" are extracted and stored automatically.

Usage Protocol: Getting the Most from Context-First

The #1 mistake: LLMs treat context_loop as optional. It's not — it's the backbone.

Built-in Enforcement (v1.2.1+)

The server ships with four compliance mechanisms that require zero configuration:

Server Instructions — Full usage protocol injected at MCP handshake via ServerOptions.instructions
Bootstrap Gate — First non-context_loop call appends a strong redirect reminder
Cross-Tool Reminders — After 3 consecutive calls without context_loop, reminders appear in tool responses
MCP Prompts — context-first-protocol and research-protocol prompt templates available on demand

Reinforce in Your System Prompt (Optional)

code

When using Context-First MCP:
1. Call context_loop BEFORE any complex task
2. Call context_loop every 2–3 tool calls
3. Call context_loop AFTER generating long-form output
4. ALWAYS follow directive.action (proceed/clarify/reset/abstain/deepen/verify)
5. Use memory_store to save findings; memory_recall to retrieve them

Research Task Workflow

research_pipeline orchestrates memory, phase control, reasoning, and autonomous file writing. It is not a web crawler — bring your own sources from web search, GitHub, fetch tools, PDFs, or any other MCP.

code

Phase 1 · Init     research_pipeline(init) → sets up state, enables autonomous file writing
Phase 2 · Gather   ONE web search → research_pipeline(gather) → file written to disk → repeat
Phase 3 · Analyze  research_pipeline(analyze) → reasoning engines produce clean analysis file
Phase 4 · Verify   research_pipeline(verify) → context health gate (non-blocking)
Phase 5 · Finalize research_pipeline(finalize) → synthesis.md + all batch files on disk

Automation shortcut:
  export_research_files(outputDir, exportVerifiedReport=true)  → write all report chunks
  export_research_files(outputDir, exportRawEvidence=true)     → write all evidence batches

Autonomous file writing is always on. Files are written to ./context-first-research-output/ by default — no LLM cooperation required. Pass outputDir to override.

Architecture

code

┌──────────────────────────────────────────────────────────────┐
│               @xjtlumedia/context-first-mcp-server            │
│                     (Core — shared logic)                     │
│                                                               │
│  Layer 1: Context Health    (9 tools)                         │
│  Layer 2: Sandbox           (3 tools)                         │
│  Layer 3: Persistent Memory (6 tools)                         │
│  Layer 4: Advanced Reasoning(5 tools)                         │
│  Layer 5: Truthfulness      (7 tools)                         │
│  State (4) · Orchestrator · Pipeline · Export                 │
└──────────────┬───────────────────────┬──────────────────────┘
               │                       │
        ┌──────▼──────┐         ┌──────▼────────┐
        │ stdio-server │         │ remote-server │
        │ (npx local)  │         │   (Vercel)    │
        │   stdio      │         │ Streamable    │
        │  37 tools    │         │    HTTP       │
        └──────────────┘         │   37 tools    │
                                 └───────────────┘

Core library (@xjtlumedia/context-first-mcp-server): All tool implementations. Zero external API keys — heuristic-based by default.
stdio-server (context-first-mcp): npx entry point, stdio transport, 37 tools.
remote-server: Vercel serverless, Streamable HTTP transport, 37 tools.

Frontend Demo

Try all 37 tools live in your browser at context-first-mcp.vercel.app.

Development

bash

git clone https://github.com/XJTLUmedia/Context-First-MCP.git
cd Context-First-MCP
pnpm install

# Build everything
pnpm build

# Run stdio server
cd packages/stdio-server && pnpm start

# Run frontend
cd packages/frontend && pnpm dev

# Tests
pnpm test

Contributing

See CONTRIBUTING.md.

License

MIT

Context-First MCP · @xjtlumedia/context-first-mcp-server · context-first-mcp

Built for every developer tired of watching their AI lose the plot.

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Install

claude_desktop_config.json

{
  "mcpServers": {
    "context-first-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "context-first-mcp@1.2.2"
      ]
    }
  }
}

Context-First MCP

Why Your AI Conversations Break Down

What You Get

Quick Start

npx — zero install

Claude Desktop

Cursor / VS Code

Remote (Streamable HTTP)

Deploy your own Vercel instance

Tool Reference

Layer 1: Core Context Health (9 tools)

Layer 1b: State Management (4 tools)

Layer 2: Sandbox & Discovery (3 tools)

Layer 3: Persistent Memory (6 tools)

Layer 4: Advanced Reasoning (5 tools)

Layer 5: Truthfulness & Verification (7 tools)

Research Pipeline & Export (2 tools)

Built on Peer-Reviewed Research

Export Helper (1 tool)

context_loop Pipeline

LLM Directive (NEW)

How context_loop Works

Usage Protocol: Getting the Most from Context-First

Built-in Enforcement (v1.2.1+)

Reinforce in Your System Prompt (Optional)

Research Task Workflow

Architecture

Frontend Demo

Development

Contributing

License

Install

How `context_loop` Works