Farnsworth gives Claude persistent memory and autonomous agent capabilities. It runs locally and provides Hierarchical Memory (Working -> Episodic -> Archival), a Multi-Model Swarm (combining Ollama models for better reasoning), and specialized agents for Web Browsing, Vision (CLIP), and Voice (Whis
An MCP server that gives Claude Code IDE-level intelligence for FastAPI projects — semantic code navigation, safe renaming, route inspection, dependency trees, and Pydantic model analysis. Instead of Claude reading files blindly, it calls structured tools backed by Jedi (Python language server) and Python's AST.
A REST API built with FastAPI that exposes endpoints via Model Context Protocol (MCP), allowing clients to interact with CRUD operations through MCP interfaces.
A FastAPI-integrated MCP server that provides mathematical operations like addition and multiplication using Pandas for data manipulation. It serves as a comprehensive example for implementing different tool registration patterns and real-time communication via Server-Sent Events.
A FastAPI library that provides Model Context Protocol tools for endpoint introspection and OpenAPI documentation, allowing AI agents to discover and understand API endpoints.
Wraps a FastAPI application as an MCP server, enabling user and task management operations through Gemini CLI tool calling with full CRUD functionality.
A minimal FastAPI implementation that mimics Model Context Protocol functionality with JSON-RPC 2.0 support. Provides basic tools like echo and text transformation through both REST and RPC endpoints for testing MCP-style interactions.
A production-ready Model Context Protocol server that provides mathematical calculations, time zone information, and message echoing capabilities through both WebSocket and HTTP endpoints. Built with FastAPI for robust API functionality and interactive documentation.
A starter template for building MCP servers with FastMCP, providing testing, linting, formatting, and NPM publishing setup.
A modern Model Control Plane (MCP) project that provides a lightweight, extensible foundation for building and deploying intelligent systems that manage and expose AI/LLM capabilities through Python, FastAPI, and Docker.
A demonstration MCP server built with FastMCP that provides basic tools (addition calculator), dynamic resources (personalized greetings), and prompt templates, designed to run on Wasmer Edge.
A minimal example server implementing the Model Context Protocol, providing addition and multiplication tools for learning and experimentation with MCP clients.
Educational example of an MCP server built with FastMCP, demonstrating how to expose tools, resources, and prompts for AI clients.
A collection of MCP servers providing utilities including user greeting, message decoding, text file reading with PDF summary generation, and NASA-quality code auditing using a trained AI model to predict software defects and measure code complexity.
Enables users to manage Gmail by fetching recent emails with pagination and creating new or reply drafts via the Gmail API. It provides a seamless interface for interacting with email content and drafting responses within Gemini-CLI.
A demonstration server that automatically generates Model Context Protocol (MCP) servers from OpenAPI specifications, providing seamless integration with FastAPI and supporting various transmission methods like STDIO, HTTP, and SSE.
Python MCP server with CI/CD tooling and testability built-in
A simple calculator MCP server that provides basic arithmetic functions (add and subtract) through HTTP transport, designed to be cloud-ready and easily deployable.
A simple example MCP server built with FastMCP and Python that provides a greeting tool, designed as a quickstart template for Smithery deployment.
A CLIP-Based Fashion Recommender system that allows users to upload clothing images and receive tags and recommendations based on visual analysis.
