generated from personal-projects/leo-claude-mktplace
Implemented MCP server core infrastructure with authentication and HTTP client: - Created auth.py for API token management - Loads GITEA_API_URL and GITEA_API_TOKEN from environment - Uses python-dotenv for .env file support - Validates required configuration on initialization - Provides authentication headers for API requests - Created client.py with base HTTP client - GiteaClient class using httpx AsyncClient - Async HTTP methods: get(), post(), patch(), delete() - Comprehensive error handling for HTTP status codes - Custom exception hierarchy for different error types - Configurable timeout (default 30s) - Updated server.py with MCP server setup - Initialized MCP server with StdioServerTransport - Integrated AuthConfig and GiteaClient - Registered placeholder tool handlers (list_repositories, create_issue, create_pull_request) - Added CLI with --help and --version options - Proper error handling for configuration failures - Updated pyproject.toml - Added console script entry point: gitea-mcp - Created comprehensive unit tests - test_auth.py: Tests for AuthConfig validation and headers - test_client.py: Tests for GiteaClient initialization and error handling All acceptance criteria met: - MCP server initializes with StdioServerTransport - Authentication loads from environment variables - Base HTTP client with auth headers implemented - Error handling for API connection failures - Server reports available tools (placeholders for future issues) Closes #2 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Gitea MCP Remote
MCP server for Gitea API integration.
Overview
This project provides a Model Context Protocol (MCP) server that enables AI assistants to interact with Gitea through its API.
Project Status
Currently in initial development. Project structure has been initialized.
Requirements
- Python >= 3.10
- Gitea instance with API access
Installation
pip install -e .
Development
Install with development dependencies:
pip install -e ".[dev]"
Run tests:
pytest
License
MIT
Description
Languages
Python
93.8%
Shell
3.2%
Dockerfile
3%