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.

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.

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.

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.

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

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.

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.

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.

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.

A basic project for developing custom MCP servers, providing a complete architectural pattern, example tools, and development infrastructure to support AI assistants in connecting to external data sources or APIs.

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.

MCP Servers Collection is a service collection based on the Model Context Protocol (MCP), which extends the capabilities of AI assistants such as Claude, enabling them to interact with external tools and services through a standardized interface.

The MCP Rust CLI server template is a simple application template that demonstrates how to implement an MCP CLI server in Rust, used to connect LLM applications with external data sources and tools.

A project that implements Server-Sent Events (SSE) based on the FastAPI framework, integrating the Model Context Protocol (MCP) function and providing a standardized interface for AI models to interact with external tools.

A curated list of hosted cloud MCP servers that provide Model Context Protocol (MCP) services accessible via simple URL endpoints to help AI applications quickly integrate external tools and data.

1MCP is a unified multi - MCP server aggregation solution designed to simplify the interaction management between AI assistants and external tools.

The JFrog Remote MCP Server integrates the capabilities of the JFrog platform into the development environment, connects AI systems with external tools through a standardized protocol, and provides functions such as resource management, artifact search, and security monitoring.

MCP Server Go is a Model Context Protocol (MCP) server implemented in Go, used to connect LLM applications with external data sources and tools, providing a simple and efficient development experience.

MCP DevTools is a set of development tools for AI assistants to interact with external services, providing seamless integration of services such as Jira and Linear

The MCP Server is a server that implements the Model Context Protocol, supporting standardized interaction between large language models and external tools and services, providing support for multiple tools, RESTful APIs, and automated documentation.

The Model Context Protocol (MCP) is a standardized protocol introduced by Anthropic for the interaction between large language models (LLMs) and external tools and functions, as well as for injecting context into LLMs. It enables models to request real-time information, perform operations in external systems, access professional knowledge bases, and interact with APIs and services. The MCP is similar to a USB hub, serving as a standard interface that reduces the risk of API changes because each MCP server is maintained by its own company, and users only need to integrate it. The MCP server provides three functions: tools, resources, and prompts.

The Model Context Protocol (MCP) is an open-source protocol designed to enable Large Language Models (LLMs) to seamlessly interact with external tools, services, and data sources through a standardized interface, simplifying the integration process and expanding AI capabilities.

This project provides multiple MCP services for Cursor IDE, connecting external tools through a standardized protocol to enhance the IDE's functionality.

This guide introduces how to build an MCP server using Python, enabling AI assistants to interact with external tools and data sources through the MCP Python SDK, including installation, quick server setup examples, testing, and deployment considerations.

The MCP Rust CLI server template is a Rust-based implementation that demonstrates how to build a CLI service compliant with the Model Context Protocol standard, facilitating the integration of LLM applications with external data sources and tools.

This project demonstrates a basic implementation of an MCP server based on .NET, including endpoints for interacting with clients, testing and debugging tools (such as MCP Inspector), and examples of integrating external services like GitLab through user secrets.

An MCP server implementation based on Flask, used to enhance the capabilities of large language models through external tools, supporting tool invocation, parameter parsing, and result injection.

This is a startup template for creating a custom MCP server for Puch AI, including job search analysis and image processing tools, supporting secure external connections and authentication.

An MCP server template based on TypeScript, providing example operations and an addition tool for communication between AI models and external tools.

This project demonstrates how to integrate Model Context Protocol (MCP) tools with Microsoft Semantic Kernel to achieve seamless interaction between AI models and external data sources or tools. By standardizing the interaction between applications and AI models through the MCP protocol and combining the powerful functions of Semantic Kernel, developers can expand AI capabilities, dynamically call external functions, and simplify the orchestration of complex workflows.