Context First MCP

Context-First MCP

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

[](https://www.npmjs.com/package/context-first-mcp) [](https://www.npmjs.com/package/context-first-mcp) [](LICENSE) [](https://modelcontextprotocol.io) [](https://smithery.ai) [](https://glama.ai/mcp/servers) [](https://nodejs.org) [](https://www.typescriptlang.org)

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:

| 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 |

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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

[](https://vercel.com/new/clone?repository-url=https://github.com/XJTLUmedia/Context-First-MCP&root-directory=packages/remote-server)

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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. |

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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 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 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.

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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:

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

Reinforce in Your System Prompt (Optional)

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.

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.

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Architecture

┌──────────────────────────────────────────────────────────────┐
│               @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.

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Frontend Demo

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

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Development

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](CONTRIBUTING.md).

License

[MIT](LICENSE)

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

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

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: XJTLUmedia
• Source: XJTLUmedia/Context-First-MCP
• License: MIT
• Homepage: https://context-first-mcp-frontend.vercel.app

Install and usage instructions live in the source repository linked above.