Which integrations are available for this server?

Provides documentation retrieval and grounded question answering for LangChain by searching official docs, extracting clean content, and returning sources. Provides documentation retrieval and grounded question answering for OpenAI by searching official docs, extracting clean content, and returning sources.

How do I use MCP Docs Intelligence Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Docs Intelligence Server What do the official Python docs say about using asyncio.gather?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP Docs Intelligence Server

by harmitprajapati2006

Overview Schema Related Servers Score Discussions

Python

Remote

📚 MCP Docs Intelligence Server

An asynchronous Model Context Protocol (MCP) server for searching, extracting, cleaning, and answering questions from official AI & Python ecosystem documentation.

✦ Overview

MCP Docs Intelligence Server is a lightweight asynchronous documentation intelligence system built around the Model Context Protocol (MCP).

Instead of asking an LLM to answer from potentially stale or incomplete internal knowledge, this project dynamically retrieves information from the official documentation of supported libraries and uses that documentation as the grounding context for the final response.

The system combines:

FastMCP — MCP server and tool interface
Serper — web search restricted to official documentation domains
Trafilatura — extraction and cleaning of useful page content
Groq — fast LLM inference
GPT-OSS-20B — response generation
httpx — asynchronous web requests
Python async/await — non-blocking architecture
python-dotenv — environment-based configuration

The result is a clean pipeline that transforms:

Natural Language Query
        ↓
Official Documentation Search
        ↓
Documentation Retrieval
        ↓
HTML → Clean Text
        ↓
Context Grounding
        ↓
Groq LLM
        ↓
Human-readable Answer

🚀 Why This Project?

Traditional documentation workflows often look like this:

Search Google
     ↓
Open multiple tabs
     ↓
Read long documentation pages
     ↓
Find the relevant section
     ↓
Understand the implementation
     ↓
Write the answer

This project compresses that workflow into a single intelligent interface:

                    ┌─────────────────────┐
                    │   Developer Query   │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │     MCP Client      │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │   FastMCP Server    │
                    │      get_docs()     │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │   Serper Search     │
                    │ Official Docs Only  │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │   Documentation     │
                    │      Pages          │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │    Trafilatura      │
                    │ HTML → Clean Text   │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │  Grounded Context   │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │       Groq          │
                    │   GPT-OSS-20B       │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │   Final Answer +    │
                    │       Sources       │
                    └─────────────────────┘

✨ Key Features

Feature	Description
🔌 MCP-native architecture	Documentation retrieval is exposed as an MCP tool
⚡ Async-first	Uses asynchronous HTTP and MCP execution
🌐 Official documentation search	Searches within trusted documentation domains
🧹 HTML cleaning	Converts complex web pages into usable text
🧠 Context-grounded generation	LLM answers are constrained to retrieved documentation
🔗 Source preservation	Source URLs are retained in the final response
🤖 Groq inference	Fast model inference through Groq
🧩 Library-aware queries	Supports different documentation ecosystems
🛠️ Simple extensibility	New libraries can be added through configuration
🔐 Environment-based secrets	API keys are loaded through `.env`
💻 CLI-friendly	User can submit queries directly from the terminal

🏗️ System Architecture

flowchart TB

    U["👨‍💻 User"]

    C["🖥️ MCP Client<br/>client.py"]

    M["🔌 FastMCP Server<br/>mcp_server.py"]

    S["🔎 Serper API"]

    D["📖 Official Documentation"]

    T["🧹 Trafilatura<br/>HTML Extraction"]

    CTX["📦 Clean Documentation Context"]

    G["⚡ Groq API<br/>GPT-OSS-20B"]

    A["💬 Grounded Answer"]

    U --> C
    C --> M
    M --> S
    S --> D
    D --> T
    T --> CTX
    CTX --> C
    C --> G
    G --> A
    A --> U

🔄 End-to-End Request Flow

sequenceDiagram

    actor User
    participant Client as MCP Client
    participant MCP as FastMCP Server
    participant Serper as Serper Search API
    participant Docs as Official Docs
    participant Extractor as Trafilatura
    participant Groq as Groq LLM

    User->>Client: Enter query + library

    Client->>MCP: call_tool("get_docs")

    MCP->>Serper: Search official documentation

    Serper-->>MCP: Search results

    loop Top documentation results
        MCP->>Docs: GET documentation page
        Docs-->>MCP: HTML content
        MCP->>Extractor: Extract readable text
        Extractor-->>MCP: Clean documentation
    end

    MCP-->>Client: Documentation context + sources

    Client->>Groq: Generate answer from context

    Groq-->>Client: Grounded response

    Client-->>User: Final answer + sources

🧠 Core Design Principle

The system follows a simple but important principle:

Retrieve first. Generate second.

The LLM is not directly responsible for discovering documentation.

Instead:

          SEARCH
             ↓
       RETRIEVE DOCS
             ↓
       CLEAN CONTENT
             ↓
      BUILD CONTEXT
             ↓
        GENERATE

This separation makes the system easier to reason about and reduces the possibility of unsupported answers.

🧩 Supported Libraries

The current implementation supports:

Library	Documentation Domain
🦜 LangChain	`python.langchain.com/docs`
🦙 LlamaIndex	`docs.llamaindex.ai/en/stable`
🟢 OpenAI	`platform.openai.com/docs`
⚡ uv	`docs.astral.sh/uv`

The mapping is maintained in mcp_server.py:

docs_urls = {
    "langchain": "python.langchain.com/docs",
    "llama-index": "docs.llamaindex.ai/en/stable",
    "openai": "platform.openai.com/docs",
    "uv": "docs.astral.sh/uv",
}

Adding a new documentation source is intentionally simple.

Example:

docs_urls = {
    "langchain": "python.langchain.com/docs",
    "llama-index": "docs.llamaindex.ai/en/stable",
    "openai": "platform.openai.com/docs",
    "uv": "docs.astral.sh/uv",
    "fastapi": "fastapi.tiangolo.com",
}

📁 Project Structure

MCP-Docs-Intelligence/
│
├── 📄 mcp_server.py
│   └── MCP server + documentation retrieval tool
│
├── 📄 client.py
│   └── User-facing MCP client
│
├── 📄 utils.py
│   └── HTML extraction + Groq LLM utility
│
├── 📄 pyproject.toml
│   └── Project metadata and dependencies
│
├── 📄 requirements.txt
│   └── pip-compatible dependency list
│
├── 📄 README.md
│   └── Project documentation
│
├── 📄 .env
│   └── Local API credentials
│
├── 📄 .gitignore
│   └── Files excluded from Git
│
└── 📁 .venv/
    └── Python virtual environment

🔧 Technology Stack

Related MCP server: MCPyDoc

FastMCP

FastMCP provides the MCP server abstraction and exposes:

@mcp.tool()
async def get_docs(query: str, library: str):

This makes documentation retrieval available as a reusable MCP tool.

Serper

Serper is used for web discovery.

The search query is restricted to the selected documentation domain:

query = f"site:{docs_urls[library]} {query}"

Example:

site:docs.astral.sh/uv How to publish a package with uv

This keeps the retrieval focused on the selected documentation ecosystem.

Trafilatura

Documentation pages contain navigation, scripts, menus, advertisements, and other irrelevant HTML.

Trafilatura extracts the useful text:

extracted = trafilatura.extract(
    html,
    include_comments=False,
    include_tables=False,
    favor_recall=False,
)

Conceptually:

Raw HTML
   │
   ├── Navigation
   ├── Scripts
   ├── CSS
   ├── Footer
   ├── Menus
   └── Documentation Content
                │
                ▼
         Trafilatura
                │
                ▼
       Clean Documentation

Groq

The final answer is generated using Groq's API.

Current model:

openai/gpt-oss-20b

The model receives:

System Instructions
        +
User Query
        +
Retrieved Documentation Context

and produces the final human-readable answer.

🔐 Environment Variables

Create a .env file in the project root:

GROQ_API_KEY=your_groq_api_key
SERPER_API_KEY=your_serper_api_key

Required credentials

Variable	Purpose
`GROQ_API_KEY`	Authenticates requests to Groq
`SERPER_API_KEY`	Authenticates web search requests

Never commit your .env file.

Add this to .gitignore:

.env
.venv/
__pycache__/
*.pyc

📦 Installation

1. Clone the repository

git clone https://github.com/your-username/mcp-docs-intelligence.git
cd mcp-docs-intelligence

2. Create a virtual environment

Windows

python -m venv .venv
.venv\Scripts\activate

Linux / macOS

python3 -m venv .venv
source .venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

Or with uv:

uv sync

📋 Dependencies

The project requires:

fastmcp>=2.12.2
groq>=0.31.1
httpx>=0.28.1
python-dotenv>=1.1.1
trafilatura>=2.0.0
mcp>=1.16.0

▶️ Running the Project

Start the client:

python client.py

The client will ask:

Enter your documentation query:

Example:

Enter your documentation query:
How do I publish a Python package with uv?

Enter library (langchain/openai/llama-index/uv):
uv

The client automatically launches the MCP server and executes the request.

🔎 Example Request

Input

Query:
How do I publish a package with uv on GitLab?

Library:
uv

Internal processing

1. Receive query
       ↓
2. Select uv documentation
       ↓
3. Build site-restricted search
       ↓
4. Query Serper
       ↓
5. Retrieve documentation pages
       ↓
6. Extract readable text
       ↓
7. Return context to client
       ↓
8. Send context to Groq
       ↓
9. Generate answer
       ↓
10. Display sources

🧠 Grounded Answer Strategy

The client uses a system prompt similar to:

Answer ONLY using the provided context.
If information is missing, say you don't know.
Keep every 'SOURCE:' line exactly.
List sources at the end.

This creates a clear separation between:

Retrieved Facts
      │
      ▼
Documentation Context
      │
      ▼
LLM Reasoning / Response Formatting

The model is instructed not to invent information that does not exist in the supplied context.

🔌 MCP Tool

The server exposes one primary tool:

get_docs(query, library)

Parameters

Parameter	Type	Description
`query`	`string`	Documentation question
`library`	`string`	Supported documentation ecosystem

Example tool call

{
  "query": "How do I create a virtual environment with uv?",
  "library": "uv"
}

Example response structure

SOURCE: https://docs.astral.sh/uv/...

Relevant documentation content...

SOURCE: https://docs.astral.sh/uv/...

Additional documentation content...

🛡️ Error Handling

The server validates the selected documentation ecosystem:

if library not in docs_urls:
    raise ValueError(
        f"Library {library} not supported by this tool"
    )

The search request also raises HTTP errors when the external API fails:

response.raise_for_status()

This prevents silent failures and makes external API problems visible.

⚙️ Async Architecture

The project uses Python's asynchronous execution model.

Web requests use:

async with httpx.AsyncClient() as client:

The MCP tool is also asynchronous:

async def get_docs(query: str, library: str):

This is important because the application is primarily I/O-bound.

Instead of blocking during network operations:

Application
     │
     ├── waiting for search API
     ├── waiting for docs page
     └── waiting for another docs page

the async architecture allows the runtime to manage I/O efficiently.

📊 Request Lifecycle

flowchart LR

    Q["Query"] --> V["Validate Library"]

    V --> S["Build site:<domain> Search"]

    S --> W["Serper"]

    W --> R["Top Search Results"]

    R --> F["Fetch URLs"]

    F --> C["Clean HTML"]

    C --> X["Build Context"]

    X --> L["Groq"]

    L --> O["Grounded Answer"]

🧱 Component Responsibilities

Component	Responsibility
`mcp_server.py`	Search, retrieval, cleaning orchestration and MCP tool exposure
`client.py`	User interaction, MCP execution and LLM call
`utils.py`	HTML extraction and Groq API wrapper
`pyproject.toml`	Package metadata and dependencies
`requirements.txt`	pip installation support
`.env`	Secrets and configuration

🧪 Example Queries

uv

How do I create and manage a virtual environment using uv?

LangChain

How do I create a tool-calling agent in LangChain?

LlamaIndex

How do I create a vector index in LlamaIndex?

OpenAI

How do I use structured outputs with the OpenAI API?

🔄 Extending the Server

The architecture is deliberately simple to extend.

Add another documentation source

Update:

docs_urls = {
    "langchain": "python.langchain.com/docs",
    "llama-index": "docs.llamaindex.ai/en/stable",
    "openai": "platform.openai.com/docs",
    "uv": "docs.astral.sh/uv",
}

Example:

"fastapi": "fastapi.tiangolo.com"

The rest of the retrieval pipeline remains unchanged.

🗺️ Future Roadmap

The current implementation establishes the foundation for a much larger documentation intelligence platform.

Phase 1 — Current

✅ MCP server
✅ Async retrieval
✅ Official documentation search
✅ HTML extraction
✅ Groq integration
✅ Source preservation
✅ Interactive client

Phase 2 — Retrieval Intelligence

⬜ Multi-result ranking
⬜ Duplicate URL removal
⬜ Query rewriting
⬜ Better document chunking
⬜ Relevance scoring
⬜ Retry / timeout strategy

Phase 3 — Advanced RAG

⬜ Embedding-based retrieval
⬜ Vector database
⬜ Semantic search
⬜ Reranking
⬜ Persistent document cache
⬜ Citation-aware generation

Phase 4 — Developer Platform

⬜ Web UI
⬜ IDE integration
⬜ VS Code extension
⬜ Documentation diffing
⬜ Version-aware documentation
⬜ API migration assistant
⬜ Code generation from docs

🔮 Possible Future Architecture

flowchart TB

    USER["Developer"]

    UI["Web / IDE / CLI"]

    MCP["MCP Gateway"]

    QUERY["Query Understanding"]

    SEARCH["Hybrid Retrieval"]

    WEB["Official Documentation"]

    CACHE["Document Cache"]

    VECTOR["Vector Database"]

    RERANK["Reranker"]

    CONTEXT["Context Builder"]

    LLM["Groq / LLM"]

    CITE["Citation Engine"]

    ANSWER["Final Developer Answer"]

    USER --> UI
    UI --> MCP
    MCP --> QUERY

    QUERY --> SEARCH

    SEARCH --> WEB
    SEARCH --> CACHE
    SEARCH --> VECTOR

    WEB --> CONTEXT
    CACHE --> CONTEXT
    VECTOR --> RERANK
    RERANK --> CONTEXT

    CONTEXT --> LLM
    LLM --> CITE
    CITE --> ANSWER
    ANSWER --> USER

🎯 Design Philosophy

This project is built around four principles:

1. Trust the source

Prefer authoritative documentation rather than arbitrary online content.

2. Retrieve before generating

The model should work from current retrieved context rather than relying entirely on memorized knowledge.

3. Keep components replaceable

Search, extraction, MCP, and LLM layers are separated so individual components can evolve independently.

4. Make the system developer-friendly

The final interface should feel like asking a senior developer who knows where the official documentation is.

🔒 Security Considerations

API keys

Never hardcode credentials:

os.getenv("GROQ_API_KEY")
os.getenv("SERPER_API_KEY")

Environment files

Do not commit:

.env

External content

Documentation retrieved from the web should be treated as external/untrusted input. Future versions should consider:

content size limits
URL allowlists
response validation
redirect validation
request retry policies
rate limiting
malicious content filtering

🧰 Troubleshooting

`ModuleNotFoundError`

Install dependencies:

pip install -r requirements.txt

`GROQ_API_KEY` error

Verify .env contains:

GROQ_API_KEY=your_key_here

and that the project is being executed from the correct directory.

`SERPER_API_KEY` error

Verify:

SERPER_API_KEY=your_key_here

MCP server appears to hang

Running:

python mcp_server.py

directly may appear to do nothing.

That is expected for a stdio MCP server because it waits for an MCP client to communicate with it.

Normally run:

python client.py

instead.

No documentation results

Check:

The selected library is supported.
The Serper API key is valid.
The documentation domain is correct.
Internet connectivity is available.

📜 Environment Example

# Search provider
SERPER_API_KEY=your_serper_api_key

# LLM provider
GROQ_API_KEY=your_groq_api_key

📌 Minimal API Flow

client.py
   │
   │ call_tool()
   ▼
mcp_server.py
   │
   │ search_web()
   ▼
Serper
   │
   │ documentation URLs
   ▼
fetch_url()
   │
   │ raw HTML
   ▼
clean_html_to_txt()
   │
   │ clean text
   ▼
MCP response
   │
   ▼
Groq GPT-OSS-20B
   │
   ▼
Final answer

🧑‍💻 Development

For local development:

git clone https://github.com/your-username/mcp-docs-intelligence.git

cd mcp-docs-intelligence

python -m venv .venv

Activate the environment and install dependencies:

pip install -r requirements.txt

Then configure .env and run:

python client.py

✅ Project Checklist

[✓] MCP server implemented
[✓] FastMCP tool exposed
[✓] Async web requests
[✓] Serper integration
[✓] Official documentation restriction
[✓] HTML extraction
[✓] Groq integration
[✓] GPT-OSS-20B support
[✓] Environment variable configuration
[✓] Interactive query input
[✓] Source preservation
[✓] GitHub documentation
[✓] Architecture diagrams

🌟 What Makes This Project Different?

This is not simply a web scraper.

It is a small documentation intelligence layer that sits between a developer and the web.

The architecture creates a clear chain of responsibility:

Developer
    ↓
Question
    ↓
MCP
    ↓
Retrieval
    ↓
Official Source
    ↓
Content Extraction
    ↓
Context
    ↓
LLM
    ↓
Answer
    ↓
Source

That separation is the foundation for turning this prototype into a larger developer knowledge infrastructure.

📈 Performance Characteristics

The application is primarily I/O-bound.

Potential latency contributors include:

Serper Search
     +
Documentation HTTP Requests
     +
HTML Extraction
     +
LLM Generation

The use of asynchronous HTTP requests reduces unnecessary blocking during network operations.

Future performance improvements can include:

Caching
Parallel fetching
Connection pooling
Result deduplication
Context compression
Persistent document storage

🤝 Contributing

Contributions are welcome.

A typical contribution workflow:

git checkout -b feature/my-feature

# Make your changes

git add .

git commit -m "feat: add documentation source"

git push origin feature/my-feature

Then open a pull request.

For larger architectural changes, document:

Problem
Proposed solution
Architectural impact
Backward compatibility
Testing strategy

📄 License

This project is licensed under the MIT License.

Add a LICENSE file containing the standard MIT License text before publishing the repository.

⭐ Acknowledgements

Built using excellent open-source and developer infrastructure technologies:

Model Context Protocol
FastMCP
Groq
Serper
Trafilatura
httpx
Python

🧭 Final Architecture Snapshot

                         ┌────────────────────┐
                         │      DEVELOPER     │
                         └─────────┬──────────┘
                                   │
                                   ▼
                         ┌────────────────────┐
                         │    MCP CLIENT      │
                         │     client.py      │
                         └─────────┬──────────┘
                                   │
                                   ▼
                    ┌────────────────────────────┐
                    │       FASTMCP SERVER       │
                    │        mcp_server.py       │
                    └─────────────┬──────────────┘
                                  │
                    ┌─────────────┴─────────────┐
                    │                           │
                    ▼                           ▼
          ┌─────────────────┐         ┌─────────────────┐
          │   SERPER API    │         │  DOCUMENTATION  │
          │  Web Discovery  │────────▶│     PAGES       │
          └─────────────────┘         └────────┬────────┘
                                               │
                                               ▼
                                     ┌────────────────────┐
                                     │    TRAFILATURA     │
                                     │   Content Cleaner  │
                                     └─────────┬──────────┘
                                               │
                                               ▼
                                     ┌────────────────────┐
                                     │  CONTEXT BUILDER   │
                                     └─────────┬──────────┘
                                               │
                                               ▼
                                     ┌────────────────────┐
                                     │       GROQ         │
                                     │    GPT-OSS-20B     │
                                     └─────────┬──────────┘
                                               │
                                               ▼
                                     ┌────────────────────┐
                                     │   GROUNDED ANSWER  │
                                     │    + SOURCES       │
                                     └────────────────────┘

⚡ Search. Extract. Ground. Answer.

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📚 MCP Docs Intelligence Server

✦ Overview

🚀 Why This Project?

✨ Key Features

🏗️ System Architecture

🔄 End-to-End Request Flow

🧠 Core Design Principle

🧩 Supported Libraries

📁 Project Structure

🔧 Technology Stack

FastMCP

Serper

Trafilatura

Groq

🔐 Environment Variables

Required credentials

📦 Installation

1. Clone the repository

2. Create a virtual environment

Windows

Linux / macOS

3. Install dependencies

📋 Dependencies

▶️ Running the Project

🔎 Example Request

Input

Internal processing

🧠 Grounded Answer Strategy

🔌 MCP Tool

Parameters

Example tool call

Example response structure

🛡️ Error Handling

⚙️ Async Architecture

📊 Request Lifecycle

🧱 Component Responsibilities

🧪 Example Queries

uv

LangChain

LlamaIndex

OpenAI

🔄 Extending the Server

Add another documentation source

🗺️ Future Roadmap

Phase 1 — Current

Phase 2 — Retrieval Intelligence

Phase 3 — Advanced RAG

Phase 4 — Developer Platform

🔮 Possible Future Architecture

🎯 Design Philosophy

1. Trust the source

2. Retrieve before generating

3. Keep components replaceable

4. Make the system developer-friendly

🔒 Security Considerations

API keys

Environment files

External content

🧰 Troubleshooting

ModuleNotFoundError

GROQ_API_KEY error

SERPER_API_KEY error

MCP server appears to hang

No documentation results

📜 Environment Example

📌 Minimal API Flow

🧑‍💻 Development

✅ Project Checklist

🌟 What Makes This Project Different?

📈 Performance Characteristics

🤝 Contributing

📄 License

⭐ Acknowledgements

🧭 Final Architecture Snapshot

⚡ Search. Extract. Ground. Answer.

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

`ModuleNotFoundError`

`GROQ_API_KEY` error

`SERPER_API_KEY` error