📚 LiveDocs MCP

Give your AI the docs it actually needs — fresh from the source, every time.

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🔎 The Problem

Every AI engineer has hit this wall:

"Claude is giving me the LangChain v0.1 API but we're on v0.3" "Why is the OpenAI example broken — oh, they changed the client in v1.0" "The CrewAI docs changed again, my code is outdated"

LLMs are trained on a snapshot of the internet. Documentation moves fast. Your AI assistant doesn't.

LiveDocs MCP bridges that gap. It connects Claude (or any MCP-compatible AI) directly to official documentation websites in real time — no stale training data, no hallucinated APIs.

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✨ Features

| Feature | Description | | -------------------------- | -------------------------------------------------------------- | | 🔴Live Docs | Fetches directly from official docs — always current | | 📦45+ Libraries | Every major AI engineering library covered | | ⚡Smart Cache | 24-hour TTL cache — fast repeat queries, no wasted API calls | | 🔁Multi-Library Search | Compare docs across frameworks side-by-side | | 💻Code Extraction | Hunts for code-block-rich pages automatically | | 🔢Version-Aware | Pin to a specific version (e.g.v0.2, 2.1.0) | | 🔑BYOK | Bring Your Own Serper API key per request | | 🌐Remote-Ready | Deploy as HTTP/SSE server — share one URL with your whole team |

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📦 Supported Libraries (45+)

<details> <summary><strong>🤖 LLM Providers</strong></summary>

| Key | Library | Description | | ----------- | ------------- | --------------------------------------- | | openai | OpenAI | GPT-4o, embeddings, DALL·E, Whisper | | anthropic | Anthropic | Claude API — messages, tool use, vision | | groq | Groq | Ultra-fast LLM inference | | mistral | Mistral AI | Mixtral, Mistral-7B | | cohere | Cohere | Command R, Embed, Rerank | | together | Together AI | Open-source model inference | | replicate | Replicate | Run open-source models via API | | google_ai | Google Gemini | Gemini Pro, Flash, embeddings |

</details> <details> <summary><strong>🔗 LLM Frameworks</strong></summary>

langchain · langgraph · langsmith · llama_index · haystack · dspy

</details> <details> <summary><strong>🕵️ Agent Frameworks</strong></summary>

crewai · autogen · semantic_kernel · smolagents · pydantic_ai

</details> <details> <summary><strong>🗄️ Vector Databases</strong></summary>

pinecone · weaviate · chromadb · qdrant · milvus · pgvector

</details> <details> <summary><strong>🧠 ML Models & Embeddings</strong></summary>

huggingface · sentence_transformers

</details> <details> <summary><strong>📊 Observability & MLOps</strong></summary>

langsmith · weights_biases · mlflow

</details> <details> <summary><strong>🌐 Web Frameworks</strong></summary>

fastapi · pydantic · django · flask

</details> <details> <summary><strong>💾 Databases & ORMs</strong></summary>

sqlalchemy · supabase · prisma · redis

</details> <details> <summary><strong>🐍 Python & DevOps</strong></summary>

uv · ruff · pytest · docker · pandas · polars

</details>

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🛠️ MCP Tools Reference

| Tool | Description | | ----------------------------------------------------- | ---------------------------------------- | | get_docs(query, library, version?, serper_api_key?) | Fetch live docs for any library | | search_docs_multi(query, libraries[]) | Search across multiple libraries at once | | get_code_examples(query, library, language?) | Targeted code snippet extraction | | list_libraries(category?) | Browse all 45+ supported libraries | | cache_stats() | View cache hits / entries / TTL |

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🚀 Quick Start

Option 1 — Remote Server (Recommended — no install needed)

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "live-docs": {
      "url": "https://live-docs-mcp.onrender.com/sse"
    }
  }
}

Config file location:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

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Option 2 — Local (Self-Hosted)

Prerequisites: Python 3.11+, uv

# 1. Clone
git clone https://github.com/NoumanZahid-85/live-docs-mcp.git
cd live-docs-mcp

# 2. Install dependencies
uv sync

# 3. Configure
cp .env.example .env
# Edit .env and add your SERPER_API_KEY (free at https://serper.dev)

# 4. Test the CLI client
uv run python mcp_client.py
# > Library: openai
# > Query: streaming chat completion

Claude Desktop config (local stdio):

{
  "mcpServers": {
    "live-docs": {
      "command": "uv",
      "args": ["run", "python", "mcp_server.py"],
      "cwd": "/absolute/path/to/live-docs-mcp",
      "env": {
        "SERPER_API_KEY": "your_serper_api_key"
      }
    }
  }
}

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🌐 Deploy Your Own Instance

Render (Free Tier)

1. Fork this repo
2. Connect to Render → New Web Service → select your fork
3. Set environment variable SERPER_API_KEY in the Render dashboard
4. Render auto-detects render.yaml and deploys

Your server will be live at https://your-service.onrender.com/sse.

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🏗️ Architecture

graph TD
    A[Claude / AI Client] -->|MCP Protocol| B[LiveDocs MCP Server]

    B --> C{Cache Hit?}
    C -->|Yes - 24h TTL| D[Return Cached Result]
    C -->|No| E[Serper Google Search API]

    E --> F[Top 5 Doc URLs]
    F --> G[Concurrent HTTP Fetch]
    G --> H[trafilatura HTML → Text]
    H --> I[Code Block Extraction]
    I --> J[Cache + Return]

    B --> K[list_libraries]
    B --> L[cache_stats]

    subgraph Tools
        B --> M[get_docs]
        B --> N[search_docs_multi]
        B --> O[get_code_examples]
    end

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🔑 API Keys

| Key | Purpose | Free Tier | Get It | | ---------------- | ------------------------- | ----------------------- | -------------------------------------------- | | SERPER_API_KEY | Google search via Serper | ✅ 2,500 searches/month | serper.dev | | GROQ_API_KEY | LLM answers in CLI client | ✅ Generous free tier | console.groq.com |

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💡 Example Prompts

Once connected in Claude, try:

"Search the LangChain docs for how to use structured output with tool calling"

"Compare how LangChain and LlamaIndex implement document chunking"

"Get me a code example for streaming with the OpenAI Assistants API"

"What changed in CrewAI v0.7?"

"List all supported vector database libraries"

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🗂️ Project Structure

live-docs-mcp/
├── mcp_server.py      # 🚀 Production MCP server (5 tools)
├── mcp_client.py      # 🖥️ Interactive CLI client
├── libraries.py       # 📋 Registry of 45+ libraries
├── cache.py           # ⚡ TTL in-memory cache
├── utils.py           # 🔧 HTML extractor + code parser
├── render.yaml        # 🌐 One-click Render deployment
├── pyproject.toml     # 📦 Dependencies (uv)
├── .env.example       # 🔑 Environment variable template
└── README.md

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🤝 Contributing

Want to add a library? Edit libraries.py — it takes 5 lines:

"your_library": {
    "site": "docs.your-library.com",
    "description": "Short description of the library",
    "category": "llm_frameworks",   # see existing categories
    "aliases": ["yl", "yourlib"],   # optional short names
},

Then open a PR. ✨

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📄 License

MIT — free to use, modify, and deploy.

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<p align="center">Built with ❤️ for AI engineers who are tired of stale docs.</p>