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Provides a learning environment for understanding and implementing Model Context Protocol (MCP) servers.
Enables AI models to interact with external tools and data sources.
Manages a comprehensive knowledge and rules suite for Tailscale networks, featuring semantic search capabilities powered by libSQL vectors and OpenAI embeddings.
Transforms mathematical content from TeX documents into a structured intermediate representation (Formath) and subsequently into Lean code.
Calculates moon phases and illumination based on date and time, providing a clear example of an MCP server.
Provides agentic retrieval-augmented generation capabilities for intelligent codebase processing via the Model Context Protocol (MCP).
Enables AI assistants to access Go documentation and package listings via the Model Context Protocol.
Enables remote execution of shell commands and access to documentation via a Model Context Protocol server.
Demonstrates and tests various mechanisms of the Model Context Protocol (MCP) using its TypeScript SDK.
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