
docs-mcp-server
Docs MCP Server: Enhance Your AI Coding Assistant
Stars: 558

The docs-mcp-server repository contains the server-side code for the documentation management system. It provides functionalities for managing, storing, and retrieving documentation files. Users can upload, update, and delete documents through the server. The server also supports user authentication and authorization to ensure secure access to the documentation system. Additionally, the server includes APIs for integrating with other systems and tools, making it a versatile solution for managing documentation in various projects and organizations.
README:
AI coding assistants often struggle with outdated documentation and hallucinations. The Docs MCP Server solves this by providing a personal, always-current knowledge base for your AI. It indexes 3rd party documentation from various sources (websites, GitHub, npm, PyPI, local files) and offers powerful, version-aware search tools via the Model Context Protocol (MCP).
This enables your AI agent to access the latest official documentation, dramatically improving the quality and reliability of generated code and integration details. It's free, open-source, runs locally for privacy, and integrates seamlessly into your development workflow.
LLM-assisted coding promises speed and efficiency, but often falls short due to:
- 🌀 Stale Knowledge: LLMs train on snapshots of the internet and quickly fall behind new library releases and API changes.
- 👻 Code Hallucinations: AI can invent plausible-looking code that is syntactically correct but functionally wrong or uses non-existent APIs.
- ❓ Version Ambiguity: Generic answers rarely account for the specific version dependencies in your project, leading to subtle bugs.
- ⏳ Verification Overhead: Developers spend valuable time double-checking AI suggestions against official documentation.
Docs MCP Server solves these problems by:
- ✅ Providing Up-to-Date Context: Fetches and indexes documentation directly from official sources (websites, GitHub, npm, PyPI, local files) on demand.
- 🎯 Delivering Version-Specific Answers: Search queries can target exact library versions, ensuring information matches your project's dependencies.
- 💡 Reducing Hallucinations: Grounds the LLM in real documentation for accurate examples and integration details.
- ⚡ Boosting Productivity: Get trustworthy answers faster, integrated directly into your AI assistant workflow.
- Accurate & Version-Aware AI Responses: Provides up-to-date, version-specific documentation to reduce AI hallucinations and improve code accuracy.
- Broad Source Compatibility: Scrapes documentation from websites, GitHub repos, package manager sites (npm, PyPI), and local file directories.
- Advanced Search & Processing: Intelligently chunks documentation semantically, generates embeddings, and combines vector similarity with full-text search.
- Flexible Embedding Models: Supports various providers including OpenAI (and compatible APIs), Google Gemini/Vertex AI, Azure OpenAI, and AWS Bedrock.
- Enterprise Authentication: Optional OAuth2/OIDC authentication with dynamic client registration for secure deployments.
- Web Interface: Easy-to-use web interface for searching and managing documentation.
- Local & Private: Runs entirely on your machine, ensuring data and queries remain private.
- Free & Open Source: Community-driven and freely available.
-
Simple Deployment: Easy setup via Docker or
npx
. - Seamless Integration: Works with MCP-compatible clients (like Claude, Cline, Roo).
What is semantic chunking?
Semantic chunking splits documentation into meaningful sections based on structure—like headings, code blocks, and tables—rather than arbitrary text size. Docs MCP Server preserves logical boundaries, keeps code and tables intact, and removes navigation clutter from HTML docs. This ensures LLMs receive coherent, context-rich information for more accurate and relevant answers.
Choose your deployment method:
Run a standalone server that includes both MCP endpoints and web interface in a single process. This is the easiest way to get started.
-
Install Docker.
-
Start the server:
docker run --rm \ -e OPENAI_API_KEY="your-openai-api-key" \ -v docs-mcp-data:/data \ -p 6280:6280 \ ghcr.io/arabold/docs-mcp-server:latest \ --protocol http --host 0.0.0.0 --port 6280
Replace
your-openai-api-key
with your actual OpenAI API key.
-
Install Node.js 22.x or later.
-
Start the server:
OPENAI_API_KEY="your-openai-api-key" npx @arabold/docs-mcp-server@latest
Replace
your-openai-api-key
with your actual OpenAI API key.This will run the server on port 6280 by default.
Add this to your MCP settings (VS Code, Claude Desktop, etc.):
{
"mcpServers": {
"docs-mcp-server": {
"type": "sse",
"url": "http://localhost:6280/sse",
"disabled": false,
"autoApprove": []
}
}
}
Alternative connection types:
Restart your AI assistant after updating the config.
Open http://localhost:6280
in your browser to manage documentation and monitor jobs.
You can also use CLI commands to interact with the local database:
# List indexed libraries
OPENAI_API_KEY="your-key" npx @arabold/docs-mcp-server@latest list
# Search documentation
OPENAI_API_KEY="your-key" npx @arabold/docs-mcp-server@latest search react "useState hook"
# Scrape new documentation (connects to running server's worker)
npx @arabold/docs-mcp-server@latest scrape react https://react.dev/reference/react --server-url http://localhost:6280/api
- Open the Web Interface at
http://localhost:6280
. - Use the "Queue New Scrape Job" form.
- Enter the documentation URL, library name, and (optionally) version.
- Click "Queue Job". Monitor progress in the Job Queue.
- Repeat for each library you want indexed.
Once a job completes, the docs are searchable via your AI assistant or the Web UI.
Benefits:
- Single command setup with both web UI and MCP server
- Persistent data storage (Docker volume or local directory)
- No repository cloning required
- Full feature access including web interface
To stop the server, press Ctrl+C
.
Run the MCP server directly embedded in your AI assistant without a separate process or web interface. This method provides MCP integration only.
Add this to your MCP settings (VS Code, Claude Desktop, etc.):
{
"mcpServers": {
"docs-mcp-server": {
"command": "npx",
"args": ["@arabold/docs-mcp-server@latest"],
"env": {
"OPENAI_API_KEY": "sk-proj-..." // Your OpenAI API key
},
"disabled": false,
"autoApprove": []
}
}
}
Replace sk-proj-...
with your OpenAI API key and restart your application.
Option 1: Use MCP Tools
Your AI assistant can index new documentation using the built-in scrape_docs
tool:
Please scrape the React documentation from https://react.dev/reference/react for library "react" version "18.x"
Option 2: Launch Web Interface
Start a temporary web interface that shares the same database:
OPENAI_API_KEY="your-key" npx @arabold/docs-mcp-server@latest web --port 6281
Then open http://localhost:6281
to manage documentation. Stop the web interface when done (Ctrl+C
).
Option 3: CLI Commands
Use CLI commands directly (avoid running scrape jobs concurrently with embedded server):
# List libraries
OPENAI_API_KEY="your-key" npx @arabold/docs-mcp-server@latest list
# Search documentation
OPENAI_API_KEY="your-key" npx @arabold/docs-mcp-server@latest search react "useState hook"
Benefits:
- Direct integration with AI assistant
- No separate server process required
- Persistent data storage in user's home directory
- Shared database with standalone server and CLI
Limitations:
- No web interface (unless launched separately)
- Documentation indexing requires MCP tools or separate commands
You can index documentation from your local filesystem by using a file://
URL as the source. This works in both the Web UI and CLI.
Examples:
- Web:
https://react.dev/reference/react
- Local file:
file:///Users/me/docs/index.html
- Local folder:
file:///Users/me/docs/my-library
Requirements:
- All files with a MIME type of
text/*
are processed. This includes HTML, Markdown, plain text, and source code files such as.js
,.ts
,.tsx
,.css
, etc. Binary files, PDFs, images, and other non-text formats are ignored. - You must use the
file://
prefix for local files/folders. - The path must be accessible to the server process.
-
If running in Docker:
- You must mount the local folder into the container and use the container path in your
file://
URL. - Example Docker run:
docker run --rm \ -e OPENAI_API_KEY="your-key" \ -v /absolute/path/to/docs:/docs:ro \ -v docs-mcp-data:/data \ ghcr.io/arabold/docs-mcp-server:latest \ scrape mylib file:///docs/my-library
- In the Web UI, enter the path as
file:///docs/my-library
(matching the container path).
- You must mount the local folder into the container and use the container path in your
See the tooltips in the Web UI and CLI help for more details.
For production deployments or when you need to scale processing, use Docker Compose to run separate services. The system selects either a local in-process worker or a remote worker client based on the configuration, ensuring consistent behavior across modes.
Start the services:
# Clone the repository (to get docker-compose.yml)
git clone https://github.com/arabold/docs-mcp-server.git
cd docs-mcp-server
# Set your environment variables
export OPENAI_API_KEY="your-key-here"
# Start all services
docker compose up -d
Service architecture:
- Worker (port 8080): Handles documentation processing jobs
-
MCP Server (port 6280): Provides
/sse
endpoint for AI tools - Web Interface (port 6281): Browser-based management interface
Configure your MCP client:
{
"mcpServers": {
"docs-mcp-server": {
"type": "sse",
"url": "http://localhost:6280/sse",
"disabled": false,
"autoApprove": []
}
}
}
Alternative connection types:
// SSE (Server-Sent Events)
"type": "sse", "url": "http://localhost:6280/sse"
// HTTP (Streamable)
"type": "http", "url": "http://localhost:6280/mcp"
Access interfaces:
- Web Interface:
http://localhost:6281
- MCP Endpoint (HTTP):
http://localhost:6280/mcp
- MCP Endpoint (SSE):
http://localhost:6280/sse
This architecture allows independent scaling of processing (workers) and user interfaces.
Commands that perform search or indexing operations require embedding configuration to be explicitly set via environment variables.
The Docs MCP Server is configured via environment variables. Set these in your shell, Docker, or MCP client config.
Variable | Description |
---|---|
DOCS_MCP_EMBEDDING_MODEL |
Embedding model to use (see below for options). |
OPENAI_API_KEY |
OpenAI API key for embeddings. |
OPENAI_API_BASE |
Custom OpenAI-compatible API endpoint (e.g., Ollama). |
GOOGLE_API_KEY |
Google API key for Gemini embeddings. |
GOOGLE_APPLICATION_CREDENTIALS |
Path to Google service account JSON for Vertex AI. |
AWS_ACCESS_KEY_ID |
AWS key for Bedrock embeddings. |
AWS_SECRET_ACCESS_KEY |
AWS secret for Bedrock embeddings. |
AWS_REGION |
AWS region for Bedrock. |
AZURE_OPENAI_API_KEY |
Azure OpenAI API key. |
AZURE_OPENAI_API_INSTANCE_NAME |
Azure OpenAI instance name. |
AZURE_OPENAI_API_DEPLOYMENT_NAME |
Azure OpenAI deployment name. |
AZURE_OPENAI_API_VERSION |
Azure OpenAI API version. |
See examples above for usage.
Set DOCS_MCP_EMBEDDING_MODEL
to one of:
-
text-embedding-3-small
(default, OpenAI) -
openai:snowflake-arctic-embed2
(OpenAI-compatible, Ollama) -
vertex:text-embedding-004
(Google Vertex AI) -
gemini:embedding-001
(Google Gemini) -
aws:amazon.titan-embed-text-v1
(AWS Bedrock) -
microsoft:text-embedding-ada-002
(Azure OpenAI) - Or any OpenAI-compatible model name
Here are complete configuration examples for different embedding providers:
OpenAI (Default):
OPENAI_API_KEY="sk-proj-your-openai-api-key" \
DOCS_MCP_EMBEDDING_MODEL="text-embedding-3-small" \
npx @arabold/docs-mcp-server@latest
Ollama (Local):
OPENAI_API_KEY="ollama" \
OPENAI_API_BASE="http://localhost:11434/v1" \
DOCS_MCP_EMBEDDING_MODEL="nomic-embed-text" \
npx @arabold/docs-mcp-server@latest
LM Studio (Local):
OPENAI_API_KEY="lmstudio" \
OPENAI_API_BASE="http://localhost:1234/v1" \
DOCS_MCP_EMBEDDING_MODEL="text-embedding-qwen3-embedding-4b" \
npx @arabold/docs-mcp-server@latest
Google Gemini:
GOOGLE_API_KEY="your-google-api-key" \
DOCS_MCP_EMBEDDING_MODEL="gemini:embedding-001" \
npx @arabold/docs-mcp-server@latest
Google Vertex AI:
GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/gcp-service-account.json" \
DOCS_MCP_EMBEDDING_MODEL="vertex:text-embedding-004" \
npx @arabold/docs-mcp-server@latest
AWS Bedrock:
AWS_ACCESS_KEY_ID="your-aws-access-key-id" \
AWS_SECRET_ACCESS_KEY="your-aws-secret-access-key" \
AWS_REGION="us-east-1" \
DOCS_MCP_EMBEDDING_MODEL="aws:amazon.titan-embed-text-v1" \
npx @arabold/docs-mcp-server@latest
Azure OpenAI:
AZURE_OPENAI_API_KEY="your-azure-openai-api-key" \
AZURE_OPENAI_API_INSTANCE_NAME="your-instance-name" \
AZURE_OPENAI_API_DEPLOYMENT_NAME="your-deployment-name" \
AZURE_OPENAI_API_VERSION="2024-02-01" \
DOCS_MCP_EMBEDDING_MODEL="microsoft:text-embedding-ada-002" \
npx @arabold/docs-mcp-server@latest
For more architectural details, see the ARCHITECTURE.md.
For enterprise authentication and security features, see the Authentication Guide.
The Docs MCP Server includes privacy-first telemetry to help improve the product. We collect anonymous usage data to understand how the tool is used and identify areas for improvement.
- Command usage patterns and success rates
- Tool execution metrics (counts, durations, error types)
- Pipeline job statistics (progress, completion rates)
- Service configuration patterns (auth enabled, read-only mode)
- Performance metrics (response times, processing efficiency)
- Protocol usage (stdio vs HTTP, transport modes)
- Search query content or user input
- URLs being scraped or accessed
- Document content or scraped data
- Authentication tokens or credentials
- Personal information or identifying data
You can disable telemetry collection entirely:
Option 1: CLI Flag
npx @arabold/docs-mcp-server@latest --no-telemetry
Option 2: Environment Variable
DOCS_MCP_TELEMETRY=false npx @arabold/docs-mcp-server@latest
Option 3: Docker
docker run -e DOCS_MCP_TELEMETRY=false ghcr.io/arabold/docs-mcp-server:latest
For more details about our telemetry practices, see the Telemetry Guide.
To develop or contribute to the Docs MCP Server:
- Fork the repository and create a feature branch.
- Follow the code conventions in ARCHITECTURE.md.
- Write clear commit messages (see Git guidelines above).
- Open a pull request with a clear description of your changes.
For questions or suggestions, open an issue.
For details on the project's architecture and design principles, please see ARCHITECTURE.md.
Notably, the vast majority of this project's code was generated by the AI assistant Cline, leveraging the capabilities of this very MCP server.
This project is licensed under the MIT License. See LICENSE for details.
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Code2Prompt is a powerful command-line tool that generates comprehensive prompts from codebases, designed to streamline interactions between developers and Large Language Models (LLMs) for code analysis, documentation, and improvement tasks. It bridges the gap between codebases and LLMs by converting projects into AI-friendly prompts, enabling users to leverage AI for various software development tasks. The tool offers features like holistic codebase representation, intelligent source tree generation, customizable prompt templates, smart token management, Gitignore integration, flexible file handling, clipboard-ready output, multiple output options, and enhanced code readability.

semantic-kernel-docs
The Microsoft Semantic Kernel Documentation GitHub repository contains technical product documentation for Semantic Kernel. It serves as the home of technical content for Microsoft products and services. Contributors can learn how to make contributions by following the Docs contributor guide. The project follows the Microsoft Open Source Code of Conduct.

anythingllm-docs
anythingllm-docs is a documentation repository for the AnythingLLM project. It contains detailed guides, setup instructions, and information on features and legal aspects of the project. The repository structure is organized into public, pages, components, and configuration files. Users can contribute by creating issues and pull requests following specific guidelines. The project is licensed under the MIT License and has been migrated to NextJS with the help of @ShadowArcanist.

RepoAgent
RepoAgent is an LLM-powered framework designed for repository-level code documentation generation. It automates the process of detecting changes in Git repositories, analyzing code structure through AST, identifying inter-object relationships, replacing Markdown content, and executing multi-threaded operations. The tool aims to assist developers in understanding and maintaining codebases by providing comprehensive documentation, ultimately improving efficiency and saving time.