docubridge

MCP server for instant access to your local documentation libraries

docubridge is a Model Context Protocol (MCP) server that gives AI assistants (like Qwen, Claude, etc.) direct access to documentation you specify. Instead of the model guessing or hallucinating API details — it reads the actual docs.

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Why?

When working with an LLM in your terminal, the model doesn't know:

• which version of FastAPI you're using
• what your internal project docs say
• any documentation you've added yourself

docubridge solves this by exposing your local Markdown documentation as MCP tools — the model can list, read, and search through them on demand.

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Features

Tool	Description
list_libraries	Show all available documentation libraries
list_files	List all files inside a specific library
get_file	Read the full content of a specific file
search_docs	Search by keyword across all docs or within a library

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Project Structure

mcp-docs-server/
├── pyproject.toml
├── .env
├── README.md
└── src/
    └── docs_server/
        ├── __init__.py
        ├── main.py
        ├── server.py
        ├── config.py
        └── reader.py

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Installation

git clone https://github.com/yourname/docubridge.git
cd docubridge
pip install -e .

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Adding Documentation

Clone any documentation that has Markdown sources. For example, FastAPI:

git clone --depth=1 --filter=blob:none --sparse https://github.com/fastapi/fastapi.git
cd fastapi
git sparse-checkout set docs/en/docs

Then move the folder into your docs/ directory:

docs/
└── fastapi/
    ├── index.md
    ├── tutorial/
    └── advanced/

You can add as many libraries as you want — just drop a folder into docs/.

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Configuration

Create a .env file in the project root:

DOCS_DIR=./docs

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Connecting to Qwen CLI

Add the following to your .qwen/settings.json:

{
  "mcpServers": {
    "docubridge": {
      "command": "docs-server",
      "timeout": 15000
    }
  }
}

Then verify the connection:

qwen mcp list

You should see docubridge in the list.

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Usage Examples

Once connected, you can ask your AI assistant:

• "What libraries do you have access to?"
• "Show me all files in the fastapi docs"
• "Find everything about dependency injection in fastapi"
• "Read the content of tutorial/path-params.md"

The model will call the appropriate tool and answer based on the actual documentation.

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Requirements

• Python 3.11+
• mcp[cli] >= 1.0.0
• pydantic-settings

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License

MIT