Enterprise MCP Documentation Server
Provides access to up-to-date Ansible documentation for automation tasks.
Provides access to up-to-date Confluence documentation for collaboration and knowledge management.
Provides access to up-to-date Docker and Docker Compose documentation for containerization.
Provides access to up-to-date Elasticsearch documentation for search and analytics.
Provides access to up-to-date Git documentation for version control.
Provides access to up-to-date GitHub documentation for version control and collaboration.
Provides access to up-to-date n8n documentation for workflow automation.
Provides access to up-to-date Ollama documentation for AI/ML model management.
Provides access to up-to-date Proxmox VE documentation for virtualization.
Provides access to up-to-date Python 3.x documentation for programming.
Provides access to up-to-date Terraform documentation for infrastructure as code.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Enterprise MCP Documentation ServerHow do I create an Elasticsearch mapping for user data?"
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.
Enterprise MCP Documentation Server
Multi-tool documentation provider for Claude Code, Cursor, and other MCP clients
Installation โข Configuration โข Usage โข Contributing
๐ Overview
Enterprise MCP Documentation Server is a powerful, self-hosted Model Context Protocol (MCP) server that provides up-to-date documentation for enterprise and development tools directly to AI coding assistants like Claude Code, Cursor, and Windsurf.
Unlike traditional approaches that rely on stale training data, this server fetches and processes live documentation from official sources, ensuring your AI assistant always has accurate, current information.
Why This Project?
๐ No More Hallucinations: Stop getting outdated API examples
๐ข Enterprise Ready: Supports internal tools like Proxmox, Nessus, TopDesk
๐ Privacy First: Self-hosted, no data leaves your infrastructure
โก Smart Caching: Redis-backed performance optimization
๐งฉ Modular Design: Easy to extend with new tools
๐ฏ Context7 Compatible: Integrates with existing solutions
Related MCP server: Dedalus MCP Documentation Server
๐ ๏ธ Supported Tools
Category | Tools | Status |
Search & Analytics | Elasticsearch | โ Complete |
Containerization | Docker, Docker Compose | โ Complete |
Programming | Python 3.x Documentation | โ Complete |
Automation | n8n Workflows | โ Complete |
AI/ML | Ollama | โ Complete |
Virtualization | Proxmox VE | โ Complete |
Security | Nessus Professional | โ Complete |
Service Management | TopDesk | โ Complete |
Collaboration | Confluence | โ Complete |
Version Control | Git, GitHub | ๐ง Planned |
Infrastructure | Terraform, Ansible | ๐ง Planned |
๐ Requirements
Python 3.9+
Redis (for caching)
4GB+ RAM (for embeddings)
Internet access (for documentation crawling)
๐ง Installation
Option 1: pip install (Recommended)
pip install enterprise-mcp-docsOption 2: Docker
# Using docker-compose (includes Redis)
git clone https://github.com/hasecon/enterprise-mcp-docs.git
cd enterprise-mcp-docs
docker-compose up -dOption 3: From Source
git clone https://github.com/hasecon/enterprise-mcp-docs.git
cd enterprise-mcp-docs
pip install -e .โ๏ธ Configuration
1. Basic Setup
# Copy configuration template
cp config/default.json config/local.json
# Set environment variables
cp .env.example .env2. Configure Tools
Edit config/local.json:
{
"tools": {
"elasticsearch": {
"provider": "Elasticsearch",
"base_url": "https://www.elastic.co/guide/en/elasticsearch/reference/current/",
"sections": ["getting-started", "search", "mapping", "query-dsl"],
"cache_ttl": 3600,
"enabled": true
},
"docker": {
"provider": "Docker",
"base_url": "https://docs.docker.com/",
"sections": ["get-started", "build", "compose"],
"cache_ttl": 7200,
"enabled": true
}
},
"vector_db": {
"enabled": true,
"embedding_model": "all-MiniLM-L6-v2"
},
"cache": {
"redis_url": "redis://localhost:6379",
"ttl": 3600
}
}3. Initialize Documentation
# Crawl all enabled tools
enterprise-mcp-docs crawl --all
# Or crawl specific tools
enterprise-mcp-docs crawl --tool elasticsearch
enterprise-mcp-docs crawl --tool docker๐ฆ Usage
Start the MCP Server
# Start server
enterprise-mcp-docs serve
# Or with custom config
enterprise-mcp-docs serve --config config/production.jsonClaude Code Integration
Add to your Claude Code configuration:
{
"mcpServers": {
"enterprise-docs": {
"command": "python",
"args": ["-m", "enterprise_mcp_docs.server"],
"env": {}
}
}
}Cursor Integration
Add to .cursor/mcp.json:
{
"mcpServers": {
"enterprise-docs": {
"command": "enterprise-mcp-docs",
"args": ["serve"]
}
}
}Example Queries
Once configured, you can ask Claude Code:
How do I create an Elasticsearch mapping for user data?
Show me Docker Compose syntax for a web application with Redis.
What are the best practices for n8n workflow error handling?
How do I configure Proxmox clustering with shared storage?๐ Adding New Providers
Create Provider Class
# src/enterprise_mcp_docs/providers/mytool.py
from .base import BaseProvider
class MyToolProvider(BaseProvider):
async def crawl_docs(self):
# Implement crawling logic
docs = []
# ... fetch and process docs
await self.add_to_vector_db(docs)
return docsAdd Configuration
{
"tools": {
"mytool": {
"provider": "MyTool",
"base_url": "https://mytool.com/docs/",
"sections": ["api", "guides"],
"cache_ttl": 3600,
"enabled": true
}
}
}Test and Submit PR!
๐งช Development
Setup Development Environment
git clone https://github.com/hasecon/enterprise-mcp-docs.git
cd enterprise-mcp-docs
# Install development dependencies
pip install -e ".[dev]"
# Install pre-commit hooks
pre-commit installRunning Tests
# Run all tests
pytest
# Run with coverage
pytest --cov=enterprise_mcp_docs
# Run specific test types
pytest -m unit
pytest -m integrationCode Quality
# Format code
black src/ tests/
isort src/ tests/
# Type checking
mypy src/
# Linting
flake8 src/ tests/๐ณ Docker Deployment
Development
docker-compose -f docker-compose.dev.yml upProduction
docker-compose -f docker-compose.prod.yml up -d๐ Monitoring
The server exposes metrics on /metrics endpoint:
Documentation fetch success/failure rates
Cache hit ratios
Search query performance
Vector similarity scores
๐ค Contributing
We love contributions! Here's how to help:
Fork the repository
Create a feature branch (
git checkout -b feature/amazing-feature)Commit your changes (
git commit -m 'Add amazing feature')Push to the branch (
git push origin feature/amazing-feature)Open a Pull Request
See CONTRIBUTING.md for detailed guidelines.
Contributors
Hasecon - Initial work
๐ License
This project is licensed under the MIT License - see the LICENSE file for details.
๐ Acknowledgments
Context7 for inspiration on documentation processing
MCP Team for the protocol
Upstash for Redis expertise
All contributors and community members
๐ Support
๐ Documentation
๐ Issues
๐ฌ Discussions
๐ง Email
โญ Star this project if you find it useful!
Made with โค๏ธ for the developer community
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