mcp-chat is an open-source and general-purpose MCP client tool for testing and evaluating MCP servers and proxies. It supports command-line interaction and web mode, can connect to various MCP servers (JS/Python/Docker), and provides functions such as chat history recording, model selection, and system prompt customization to help developers debug MCP services.

MCP is an open-source protocol for standardizing LLM interactions, providing a unified framework for connecting data sources, obtaining context, using tools, and executing standard prompts. Project examples demonstrate how to build MCP servers and clients to implement knowledge base chatbot functionality.

A Model Context Protocol (MCP) implementation in Haskell, supporting interaction with clients such as Claude through StdIO and JSON-RPC, and providing resource, tool, and prompt management functions.

A production-ready MCP server template for hosting custom AI tools and prompt templates, supporting remote deployment and local operation, helping developers quickly build AI model interaction tools.

Agent Care is a server based on the MCP protocol, providing medical tools and interaction prompts for electronic medical record systems such as Cerner and Epic, and supporting access to FHIR data and medical resources.

A Python-based MCP server that connects to a SQLite database and provides data query, schema access, and prompt template functions, supporting interaction with language models and MCP clients.

The MCP Python SDK is a Python library that implements the Model Context Protocol (MCP), used to standardize the interaction between LLM applications and servers, supporting the exposure and management of resources, tools, and prompts.

The OracleDB MCP Server is a server that provides Oracle database table/column context to LLMs, supporting interaction with the database through LLM prompts, generating SQL statements, and returning results.

The Golang MCP Server SDK is a server-side development toolkit for building servers that comply with the Model Context Protocol (MCP) specification, supporting interaction with LLMs through standard transport methods (such as stdio and Server-Sent Events), and providing core functions such as resources, tools, and prompts.

The MCP Python SDK is a Python library that implements the Model Context Protocol (MCP) for standardizing the interaction between LLM applications and servers, supporting the exposure and management of resources, tools, and prompts.

This project guides you on how to build a client-host-server system based on the MCP protocol, including a FastAPI server, an MCP client, and an SSE streaming server. It enables the interaction of resources, tools, and prompts and supports integration with the Claude desktop application.

This project is a Go implementation of the Model Context Protocol (MCP), a JSON-RPC based protocol for client-server interaction in LLM applications. The project includes Go structure definitions, client implementation, and example code, supporting interaction with resources, tools, prompts, etc.

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 MCP tutorial introduces how to use an MCP server and client for interaction, including resource discovery, prompt definition, and image return functions.

MCPOmni Connect is a powerful and universal command-line interface (CLI) that serves as the gateway to the Model Context Protocol (MCP) ecosystem. It integrates multiple MCP servers, AI models, and transport protocols, providing a unified and intelligent interface that supports multi-protocol connection, AI-driven interaction, security and privacy protection, memory management, and advanced prompt processing. etc. capabilities.

The MCP protocol standardizes the interaction between applications and LLMs, and realizes unified management of tool invocation, resource access, and prompt templates through a modular architecture (server, client, host). This project demonstrates how to build an MCP server and client that support knowledge base queries, context selection, and analysis processes.

An MCP server implementation based on Node.js and TypeScript, providing a standardized LLM context interaction protocol, including functions such as calculation tools, system information, message reflection, and prompt templates.

This project is a server based on the Model Context Protocol (MCP) that provides natural language interaction access to the art collections of the Art Institute of Chicago. It allows AI models to search for artworks in the Art Institute of Chicago and use these works as resources. The server provides various tools, including searching by title, getting specific artworks, full - text search, artist search, finding artworks by artist, etc., as well as a prompt for generating an interactive art gallery.

The Xircuits MCP Component Library is a visual programming tool library for implementing Model Context Protocol (MCP) servers. It provides components for creating, configuring, and running MCP servers, and supports interactions with LLM applications through resources, tools, and prompt templates.

Overview: MCP (Model Context Protocol) is a framework for AI automation through a standardized protocol, which includes key components such as hosts, clients, servers, local data sources, and remote services. The core lies in the server, which provides context, tools, and prompts for clients. MCP enables AI agents to automate tasks, such as code generation, debugging, and interaction with external services like GitHub repositories.