MCP Servers Collection
A collection of MCP servers providing structured interfaces for AI assistants to interact with various development tools and services.
MCP Servers Collection
A collection of Model Context Protocol (MCP) servers for enhancing AI tooling capabilities. These servers provide structured interfaces for AI assistants to interact with various development tools and services.
What is MCP?
The Model Context Protocol (MCP) is an open standard that enables AI assistants to securely connect to external data sources and tools. MCP servers expose specific functionality as tools that AI assistants can discover and use.
Available Servers
š Overmind Server
Path: overmind/
Purpose: Process management for Procfile-based applications using Overmind
Installation:
uvx --from git+https://github.com/geoffjay/mcp-servers#subdirectory=overmind mcp-server-overmind
š Fundamental Analysis Server
Path: fundamental_analysis/
Purpose: Performs fundamental analysis for a given stock code using the Finnhub API.
Installation:
uvx --from git+https://github.com/geoffjay/mcp-servers#subdirectory=fundamental_analysis mcp-server-fundamental-analysis
Key Features:
- Start, stop, and restart processes defined in Procfiles
- Monitor process status and health
- Execute commands within the Overmind environment
- Socket-based detection of running instances
- Support for custom Procfile locations and formations
Tools Available:
overmind_start- Start Overmind with optional configurationovermind_stop- Stop specific processes or all processesovermind_restart- Restart specified processesovermind_status- Get status of all processesovermind_quit- Gracefully quit Overmindovermind_kill- Forcefully kill all processesovermind_run- Run commands in Overmind environmentovermind_connect- Get connection instructions for processesovermind_is_running- Check if Overmind is runningovermind_check_procfile- Validate Procfile existence and contentsovermind_find_procfiles- Find all Procfiles in a directory tree
Requirements: Overmind and tmux must be installed on the system.
Testing: The overmind/test_environment/ directory contains a complete test setup with sample processes and testing utilities. Run ./overmind/test_environment/run_tests.sh for comprehensive testing.
Quick Start
Using with Claude Desktop
Add any of these servers to your Claude Desktop configuration:
{
"mcpServers": {
"overmind": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/geoffjay/mcp-servers#subdirectory=overmind",
"mcp-server-overmind"
]
}
}
}
Using with Other MCP Clients
Each server can be executed directly:
# Overmind server
uvx --from git+https://github.com/geoffjay/mcp-servers#subdirectory=overmind mcp-server-overmind
Development
Prerequisites
- Python 3.10 or higher
- uv for dependency management
- direnv (optional, for automatic environment activation)
Project Structure
mcp-servers/
āāā README.md # This file
āāā overmind/ # Overmind process manager server
ā āāā src/mcp_server_overmind/
ā āāā tests/
ā āāā pyproject.toml
ā āāā .envrc
ā āāā README.md
āāā [future-servers]/ # Additional servers will be added here
Adding a New Server
- Create a new directory for your server
- Set up the project structure:
your-server/ āāā src/mcp_server_yourname/ ā āāā __init__.py ā āāā server.py āāā tests/ āāā pyproject.toml āāā .envrc āāā README.md - Configure
pyproject.tomlwith:- Package name:
mcp-server-yourname - Entry point:
mcp-server-yourname = "mcp_server_yourname.server:main" - Dependencies including
mcp>=1.2.0
- Package name:
- Implement your server using the FastMCP framework
- Add comprehensive tests
- Document your server in its README.md
- Update this main README.md to include your server
Running Tests
Each server includes its own test suite:
cd your-server-directory
uv run --extra dev pytest tests/ -v
Building and Testing Locally
cd your-server-directory
uv build
uv run your-entry-point
Contributing
We welcome contributions! Please:
- Follow the existing project structure and patterns
- Include comprehensive tests for new functionality
- Add thorough documentation
- Ensure your server follows MCP best practices
- Update this README when adding new servers
Code Style
- Use Python type hints throughout
- Follow async/await patterns for I/O operations
- Include docstrings for all public functions
- Use descriptive tool names and clear parameter documentation
Testing Requirements
- Achieve high test coverage (aim for >90%)
- Include both unit and integration tests
- Mock external dependencies appropriately
- Test both success and failure scenarios
License
This project is licensed under the MIT License. See individual server directories for specific license information.
Resources
Support
For issues with specific servers, please refer to their individual README files. For general project issues or questions, please open an issue in this repository.
Server Status
| Server | Status | Version | Last Updated |
|---|---|---|---|
| overmind | ā Stable | 0.1.0 | January 2025 |
| fundamental_analysis | ā Stable | 0.1.0 | January 2025 |
Roadmap
Future servers under consideration:
- Development environment setup
- Audio processing (MIDI/OSC)
Related Servers
weibaohui/kom
Provides multi-cluster Kubernetes management and operations using MCP, It can be integrated as an SDK into your own project and includes nearly 50 built-in tools covering common DevOps and development scenarios. Supports both standard and CRD resources.
AWS CDK MCP Server
Offers guidance and tools for AWS CDK, covering best practices, security compliance with CDK Nag, infrastructure validation, and pattern discovery.
Feishu API
Fetches API information from Feishu OpenAPI for seamless integration and management within an IDE.
Pica MCP Server
An MCP server for Pica that enables seamless interaction with various third-party services through a standardized interface.
Togello MCP Server
An MCP server for managing application context using the Togello API.
Remote MCP Server (Authless)
An example of a remote MCP server deployable on Cloudflare Workers, without authentication.
ActionKit MCP Starter
A demonstration server for ActionKit, providing access to Slack actions via Claude Desktop.
VSCode MCP
Interact with VSCode through the Model Context Protocol, enabling AI agents to perform development tasks.
Windows CLI
Interact with Windows command-line interfaces like PowerShell, CMD, Git Bash, and WSL.
Postman Tool Generation
Generates AI agent tools from Postman collections and requests using the Postman API.