MCP Go is a server-side framework for the Model Context Protocol (MCP) implemented in Go, used to connect LLM applications with external data sources and tools. It provides core functions such as resource management, tool invocation, and prompt templates, and supports session management and middleware extension.

emcee is a tool that provides a Model Context Protocol (MCP) server for any web application with an OpenAPI specification, connecting applications such as Claude Desktop to external tools and data services.

A prototype of the MCP server based on Uber Eats, enabling seamless integration between LLM applications and external tools

The Quarkus Model Context Protocol (MCP) Server is an open-source protocol server that supports easy integration of LLM applications with external data sources and tools through declarative and programmatic APIs.

The Kubernetes MCP Server is a powerful implementation of the Kubernetes model context protocol, supporting operations on Kubernetes and OpenShift clusters. It can perform various resource management tasks without relying on external tools.

A TypeScript-based MCP server template that provides sample operations and an addition tool for communication between AI models and external tools.

A chain of thought MCP server that uses the Groq API to call the Qwen model, enhancing the ability of AI to handle complex tasks through external thinking tools.

This project provides multiple Model Context Protocol servers, including web search, weather query, and memory storage functions, enabling AI models to integrate external tools and data sources.

Hello-MCP is a practical tool that helps Claude Desktop interact with external tools through the MCP protocol, providing a simple configuration wizard and integration features.

The Pica MCP Server is a Model Context Protocol server built on TypeScript, providing the Pica platform with the ability to securely connect AI assistants to external tools and data sources.

Ollama Chat with MCP is a project that demonstrates how to integrate a local language model with real-time web search functionality through the Model Context Protocol (MCP). It includes an MCP web search server, a terminal client, and a Gradio-based web front-end, enabling locally run LLMs to access external tools and data sources.

BinjaLattice is a secure communication protocol designed for Binary Ninja, supporting interaction with external MCP servers and tools, and providing functions for obtaining and modifying binary analysis data.

A Rust-based MCP CLI server template for implementing standardized connections between LLM applications and external data sources and tools.

OpenAPI-MCP is a proxy server that can dynamically convert OpenAPI specifications into standardized MCP tools, resources, and prompts, simplifying the integration process between AI agents and external APIs.

OpenAPI-MCP is a proxy server that dynamically converts OpenAPI specifications into MCP tools, enabling AI agents to access external APIs without custom encapsulation.

The Scrapeless MCP Server is an open - source protocol project that seamlessly integrates LLM applications with external data sources and tools (such as Google Search) in a standardized way, providing dynamic context retrieval capabilities.

The JS MCP Server is a server component based on JavaScript and WASM, providing an easy - to - integrate SDK and supporting custom processors (tools) and interactions with external services (such as Postgres and HTTP).

The Browserbase MCP Server is an open - source project based on the Model Context Protocol (MCP), providing cloud browser automation capabilities, supporting web page interaction, data extraction, screenshot taking, and JavaScript execution, etc., to help LLM applications seamlessly integrate with external data sources and tools.

A project that implements Server-Sent Events (SSE) based on the Starlette framework, integrates the Model Context Protocol (MCP), and provides a standard interface for AI models to interact with external tools.

The MCP portal is the official community platform for the Model Context Protocol, providing resources such as documentation, practical guides, server implementations, and tool integrations. It supports AI models to access external tools through the MCP protocol, covering a wide range of scenarios from database connections to multimodal applications.