A foundational template for building MCP servers in Python using Streamable HTTP transport. Provides example implementations of tools, resources, and prompts to help developers create custom MCP integrations for AI assistants.
A template and demonstration project for building, testing, and deploying remote MCP servers using FastMCP and uv. It provides a foundational structure for creating MCP-compliant tools that can be hosted publicly and integrated with LLM agents.
A plugin-based MCP server built on FastAPI that supports dynamic tool loading, hot reloading, and API key authentication for extensible AI tool integrations.