An educational MCP server example built with FastMCP that demonstrates how to expose tools, resources, and prompts to AI clients. Provides a learning foundation for building MCP servers with Python and integrating them with AI applications like IDEs and chatbots.
Provides a template for building an MCP server with example tools, prompts, and resources, enabling developers to create a server that exposes data and actions to AI clients.
Enables learning and integration testing of the Model Context Protocol by evolving from a minimal server toward production-grade tools, resources, prompts, and agent interoperability.
Enables MCP-compatible clients to manage and run local or cloud LLMs, generate completions and embeddings, and perform web search/fetch through exposed tools.
Exposes GitHub, SQLite database, and calendar tools, resources, and prompts over MCP so any MCP client can query data, manage issues, and schedule events.