mcp-docs

mcp-docs

Generic MCP server that exposes Markdown documentation to LLMs, enabling them to search and answer questions about any software documentation.

Category
Visit Server

README

mcp-docs

Generic MCP server that exposes Markdown documentation to LLMs, enabling them to search and answer questions about any software documentation.

The server identity (name, instructions, category labels) is driven entirely by the docs/ directory — which is a separate repository cloned alongside this one.

Requirements

  • Python 3.14+
  • uv

Setup

1. Clone the documentation repository

The docs/ directory must exist before the server can start. Clone the documentation repository into it:

git clone <docs-repo-url> docs

2. Install dependencies

uv sync

3. Configure environment variables (optional)

Copy .env.example to .env and set MCP_DOCS_DIR if your documentation directory lives outside docs/:

cp .env.example .env
Variable Default Description
MCP_DOCS_DIR ./docs Absolute or relative path to the documentation directory

docs/ directory structure

The server auto-discovers categories from subdirectories. The only required file is config.toml at the root of docs/.

docs/
├── config.toml          # required — project identity
├── <category>/
│   ├── <topic>.md
│   └── ...
└── <category>/
    └── ...

config.toml

[project]
name = "my-project"
instructions = """
System instructions for the LLM. Describe what this documentation covers
and how the model should use the available tools.
"""

[categories]
folder-name = "Human-readable label"
  • [project] is required. name identifies the server; instructions guides the LLM.
  • [categories] is optional. Any subdirectory not listed gets a title-cased label automatically (my-folderMy Folder).
  • Files inside img/ subdirectories are never served.

Usage

Run the server (stdio mode)

uv run main.py

Development with MCP Inspector

uv run mcp dev main.py

Opens the MCP Inspector in the browser. To connect to a running SSE or Streamable HTTP server, start it first and point the inspector to the printed endpoint:

uv run main.py --transport sse
uv run main.py --transport streamable-http

Configure with Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "my-project": {
      "command": "uv",
      "args": ["--directory", "/path/to/mcp-docs", "run", "main.py"],
      "env": { "MCP_DOCS_DIR": "/path/to/your/docs" }
    }
  }
}

Configure with Claude Code

claude mcp add my-project -- uv --directory /path/to/mcp-docs run main.py

To pass MCP_DOCS_DIR when using Claude Code, add it to the server environment:

claude mcp add my-project -e MCP_DOCS_DIR=/path/to/your/docs -- uv --directory /path/to/mcp-docs run main.py

Containers

Build da imagem

# Docker
docker build -f .containers/Dockerfile -t mcp-docs:latest .

# Podman
podman build -f .containers/Dockerfile -t mcp-docs:latest .

Executar localmente com docker-compose

Certifique-se de que o diretório docs/ existe e está populado antes de iniciar:

docker compose up

# ou com Podman
podman compose up

O servidor ficará disponível em http://localhost:8080/mcp.

Executar o container manualmente

# Docker
docker run --rm -p 8080:8080 -v ./docs:/docs:ro mcp-docs:latest

# Podman
podman run --rm -p 8080:8080 -v ./docs:/docs:ro mcp-docs:latest

Deploy no Kubernetes

Os arquivos de deployment e service estão em .containers/. Edite image: em deployment.yaml para apontar para o seu registry antes de aplicar.

kubectl apply -f .containers/deployment.yaml
kubectl apply -f .containers/service.yaml

O Deployment referencia um PersistentVolumeClaim chamado mcp-docs-pvc para montar os arquivos de documentação em /docs. Crie e popule o PVC com a documentação antes de aplicar o Deployment.

Testando com o MCP Inspector

Com o container rodando localmente (via docker compose up ou manualmente), o endpoint Streamable HTTP estará em http://localhost:8080/mcp.

  1. Abra o MCP Inspector:

    npx @modelcontextprotocol/inspector
    
  2. No inspector, selecione o transport Streamable HTTP.

  3. Informe a URL: http://localhost:8080/mcp

  4. Clique em Connect.

  5. Teste as ferramentas disponíveis: list_docs, read_doc, search_docs.

Capabilities

Tools

Tool Description
list_docs(category?) List available documentation files, optionally filtered by category
read_doc(category, topic) Read the full content of a documentation file
search_docs(query, category?) Full-text search across all documentation

Resources

URI Description
docs://index Full index of all available documentation files
docs://{category}/{topic} Content of a specific documentation file

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured