Context First MCP

Context-First MCP

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

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.

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Why Your AI Conversations Break Down

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

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What You Get

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

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.

{

"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"] } }

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Quick Start

npx — zero install

npx context-first-mcp

Claude Desktop

{

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

Cursor / VS Code

{

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

Remote (Streamable HTTP)

{

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

Deploy your own Vercel instance

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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)

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Built on Peer-Reviewed Research

Every core algorithm traces back to a published paper:

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)

context_loop Pipeline

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:

{

"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"] } }

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How context_loop Works

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 co