DevDocs MCP

by llmian-space

Integrations

  • Used for property-based testing to ensure URI template validation, parameter extraction correctness, error handling robustness, and type safety enforcement

  • Provides type-safe parameter handling for the resource template system, enabling validation and extraction of parameters from URIs

  • Supports the testing infrastructure, allowing for property-based and integration tests to validate the functionality of the documentation management system

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

  • Basic project structure
  • Resource template system
  • Property-based testing infrastructure
  • URI validation and parameter extraction
  • 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

  1. Follow TDD approach:
    • Write property-based tests first
    • Implement minimal passing code
    • Refactor for clarity and efficiency
  2. Error Handling:
    • Use structured error types
    • Implement recovery strategies
    • Maintain system stability
  3. 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

  1. Create feature branch
  2. Add property tests
  3. Implement feature
  4. Update documentation
  5. Submit pull request

Next Steps

  1. Implement documentation processor integration
  2. Add caching layer with proper lifecycle management
  3. Develop task management system
  4. 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/
-
security - not tested
A
license - permissive license
-
quality - not tested

A Model Context Protocol implementation that enables AI-powered access to documentation resources, featuring URI-based navigation, template matching, and structured documentation management.

  1. Project Structure
    1. Core Components
      1. Resource Template System
      2. Testing Strategy
    2. Implementation Progress
      1. Completed
      2. In Progress
      3. Planned
    3. Development Guidelines
      1. Branch Management
        1. Contributing
          1. Next Steps
            1. Support Resources

              Related MCP Servers

              • -
                security
                F
                license
                -
                quality
                A Model Context Protocol server utilizing Claude AI for generating intelligent queries and offering documentation assistance based on API documentation analysis.
                Last updated -
                3
                2
                TypeScript
              • A
                security
                A
                license
                A
                quality
                A Model Context Protocol implementation that enables AI assistants to interact with markdown documentation files, providing capabilities for document management, metadata handling, search, and documentation health analysis.
                Last updated -
                14
                346
                11
                TypeScript
                MIT License
                • Apple
                • Linux
              • A
                security
                A
                license
                A
                quality
                A Model Context Protocol server that enables AI assistants like Claude to interact with Outline document services, supporting document searching, reading, creation, editing, and comment management.
                Last updated -
                25
                1
                Python
                MIT License
              • A
                security
                A
                license
                A
                quality
                A Model Context Protocol server that enables AI assistants to interact with Confluence content, supporting operations like retrieving, searching, creating, and updating pages and spaces.
                Last updated -
                9
                3
                TypeScript
                MIT License

              View all related MCP servers

              ID: 3ccn0tqnhm