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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.

Provides secure filesystem operations, HTTP fetching with SSRF protection, JSON validation, artifact logging, and optional Redis key-value storage through both stdio and HTTP transports. Features production-ready security controls including sandbox enforcement, allowlist validation, and comprehensive input validation.

Fast MCP Proper

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.

A simple Model Context Protocol (MCP) server that allows GitHub Copilot to access custom tools, including an example tool to return the author name.

A server that automatically reviews code style and suggests improvements, with special focus on Flutter/Dart projects and general programming styles.

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.

Enables AI assistants to convert, validate, and deploy websites to the Fast Mode CMS platform while managing content collections and schemas. It provides tools for template validation, CMS item management, and automated site deployment directly from the model context.

fast-pyairbyte lets you create a data pipeline in code ,from any Airbyte connector, with a single prompt.

Provides access to Fathom meeting recordings, enabling users to list meetings with filters, retrieve AI-generated summaries, and access full transcripts with speaker attribution and timestamps.

Faucet94 Super Win CLI MCP Server

全面解析Faucet94 Super Win Cli MCP ServerMCP Server的核心功能、安装配置和实用案例。作为顶级Model Context Protocol服务器，Faucet94 Super Win Cli MCP Server能让AI助手访问实时数据、执行操作，为您提供更智能的工作体验和自动化解决方案。

安装状态

已补齐

A temporal knowledge graph system that enables users to record and query architectural decisions, implementation patterns, and project failures. It integrates with Claude to provide hybrid search, timeline tracking, and automated knowledge gap detection using graph analysis.

Enables AI assistants to send real faxes through the FaxDrop API. Provides safeguards including rate limiting, dry-run mode, and audit logging for secure fax operations.