Context-First-MCP

<div align="center"> # 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) ```bash npx context-first-mcp ``` *Works instantly with Claude Desktop · Cursor · VS Code · any MCP client · Vercel remote — zero API keys needed.* </div> --- > **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: ``` 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 [](https://vercel.com/new/clone?repository-url=https://github.com/XJTLUmedia/Context-First-MCP&root-directory=packages/remote-server) --- ## 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](https://arxiv.org/abs/2506.01056) | `discover_tools` | | ScaleMCP | Semantic Tool Grouping | [2505.06416](https://arxiv.org/abs/2505.06416) | `discover_tools` registry | | ERGO | Entropy-based Quality | [2510.14077](https://arxiv.org/abs/2510.14077) | `entropy_monitor` | | RLAAR | Calibrated Abstention | [2510.18731](https://arxiv.org/abs/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 ``` 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 ``` 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: 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. --- ## 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. --- ## Frontend Demo Try all 37 tools live in your browser at **[context-first-mcp.vercel.app](https://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](CONTRIBUTING.md). ## License [MIT](LICENSE) --- <div align="center"> **Context-First MCP** · [@xjtlumedia/context-first-mcp-server](https://www.npmjs.com/package/@xjtlumedia/context-first-mcp-server) · [context-first-mcp](https://www.npmjs.com/package/context-first-mcp) *Built for every developer tired of watching their AI lose the plot.* </div>