DevDocs MCP Implementation
A Model Context Protocol (MCP) implementation for documentation management and integration.
Project Structure
src/
├── resources/
│ ├── templates/ # Resource template system
│ └── managers/ # Resource management
├── documentation/
│ ├── processors/ # Documentation processing
│ └── integrators/ # Integration handlers
├── tasks/
│ ├── issues/ # Issue tracking
│ └── reviews/ # Review management
└── tests/
├── property/ # Property-based tests
└── integration/ # Integration tests
Core Components
Resource Template System
The resource template system provides URI-based access to documentation resources with:
- Type-safe parameter handling through Pydantic
- Flexible URI template matching
- Comprehensive error handling
- State management for resource lifecycle
Example usage:
from src.resources.templates.base import ResourceTemplate
# Create a template with parameter typing
template = ResourceTemplate(
uri_template='docs://api/{version}/endpoint',
parameter_types={'version': str}
)
# Extract and validate parameters
params = template.extract_parameters('docs://api/v1/endpoint')
template.validate_parameters(params)
Testing Strategy
The project uses property-based testing with Hypothesis to ensure:
- URI template validation
- Parameter extraction correctness
- Error handling robustness
- Type safety enforcement
Run tests:
pytest tests/property/test_templates.py
Implementation Progress
Completed
- [x] Basic project structure
- [x] Resource template system
- [x] Property-based testing infrastructure
- [x] URI validation and parameter extraction
- [x] Error handling foundation
In Progress
- [ ] Documentation processor integration
- [ ] Caching layer implementation
- [ ] Task management system
- [ ] Performance optimization
Planned
- [ ] Search implementation
- [ ] Branch mapping system
- [ ] State tracking
- [ ] Monitoring system
Development Guidelines
Follow TDD approach:
- Write property-based tests first
- Implement minimal passing code
- Refactor for clarity and efficiency
Error Handling:
- Use structured error types
- Implement recovery strategies
- Maintain system stability
Documentation:
- Keep README updated
- Document new features
- Include usage examples
Branch Management
The project uses a branch-based development approach for:
- Feature tracking
- Documentation integration
- Task management
- Progress monitoring
Contributing
- Create feature branch
- Add property tests
- Implement feature
- Update documentation
- Submit pull request
Next Steps
- Implement documentation processor integration
- Add caching layer with proper lifecycle management
- Develop task management system
- Create monitoring and performance metrics
Support Resources
- MCP Concepts:
mcp-docs/docs/concepts/
- Python SDK:
python-sdk/src/mcp/
- Example Servers:
python-sdk/examples/servers/
Documentation Management and Integration
Project Details
- llmian-space/devdocs-mcp
- MIT License
- Last Updated: 3/30/2025
Categories
Recomended MCP Servers
Node.js/TypeScript MCP server for Atlassian Bitbucket. Enables AI systems (LLMs) to interact with workspaces, repositories, and pull requests...
The Shodan MCP Server by ADEO Cybersecurity Services provides cybersecurity professionals with streamlined access to Shodan's powerful reconnaissance...
ClickUp MCP Server - Integrate ClickUp task management with AI through Model Context Protocol
A MCP server that enables Claude to discover and call any API endpoint through semantic search. Intelligently chunks...
Official Firecrawl MCP Server - Adds powerful web scraping to Cursor, Claude and any other LLM clients.
Connect to MCP servers that run on SSE transport, or expose stdio servers as an SSE server using...
MCP Server for the Perplexity API.
MCP server enabling persistent memory for Claude through a local knowledge graph - fork focused on local development
Repository for MCP screenshot functionality
MCP server for Unreal Engine 5