Model Context Protocol (MCP) Servers

0
0 Reviews
1 Stars
This repository offers MCP server implementations across multiple languages, facilitating AI communication with tools through a consistent protocol, primarily for VS Code integration.
Added on:
Created by:
Model Context Protocol (MCP) Servers

Model Context Protocol (MCP) Servers

0 Reviews
1
0
Model Context Protocol (MCP) Servers
This repository offers MCP server implementations across multiple languages, facilitating AI communication with tools through a consistent protocol, primarily for VS Code integration.
Added on:
Created by:
Apr 23 2025
Christos Ploutarchou
Featured

What is Model Context Protocol (MCP) Servers?

The MCP servers implement the Model Context Protocol, allowing AI agents to interact seamlessly with various tools and services. The Python server is fully operational, supporting comprehensive features like SSE, JSON-RPC, error handling, and monitoring. The Go and Rust servers are in progress, aiming to provide core MCP functionalities and integration capabilities. These servers enable real-time data exchange, diagnostics, and enhanced developer support in AI toolchains, ensuring standardized communication between AI and external systems.

Who will use Model Context Protocol (MCP) Servers?

  • AI developers
  • Tool integrators
  • VS Code users
  • AI research teams

How to use the Model Context Protocol (MCP) Servers?

  • Step1: Install Docker and Docker Compose
  • Step2: Clone the repository from GitHub
  • Step3: Run 'docker-compose up' to start servers
  • Step4: Configure your IDE (e.g., VS Code) to connect to the server endpoint
  • Step5: Use the MCP protocol features via supported tools

Model Context Protocol (MCP) Servers's Core Features & Benefits

The Core Features
  • Supports MCP protocol in Python, Go, and Rust
  • Provides endpoints for SSE, JSON-RPC, and health checks
  • Includes monitoring and error handling features
  • Supports integration with VS Code
The Benefits
  • Enables standardized AI-tool communication
  • Supports real-time messaging and diagnostics
  • Facilitates multi-language server implementations
  • Enhances developer productivity and tool interoperability

Model Context Protocol (MCP) Servers's Main Use Cases & Applications

  • AI tool integration in IDEs like VS Code
  • Automated tool management for AI systems
  • Development and testing of custom MCP servers
  • Real-time AI debugging and diagnostics

FAQs of Model Context Protocol (MCP) Servers

Developer

You may also like:

Developer Tools

A desktop application for managing server and client interactions with comprehensive functionalities.
A Model Context Protocol server for Eagle that manages data exchange between Eagle app and data sources.
A chat-based client that integrates and uses various MCP tools directly within a chat environment for enhanced productivity.
A Docker image hosting multiple MCP servers accessible through a unified entry point with supergateway integration.
Provides access to YNAB account balances, transactions, and transaction creation through MCP protocol.
A fast, scalable MCP server for managing real-time multi-client Zerodha trading operations.
A remote SSH client facilitating secure, proxy-based access to MCP servers for remote tool utilization.
A Spring-based MCP server integrating AI capabilities for managing and processing Minecraft mod communication protocols.
A minimalistic MCP client with essential chat features, supporting multiple models and contextual interactions.
A secure MCP server enabling AI agents to interact with Authenticator App for 2FA codes and passwords.

AI Chatbot

A server implementation supporting Model Context Protocol, integrating CRIC's industrial AI capabilities.
Provides MCP servers in Python, Go, and Rust for seamless AI tool integration in VS Code.
Implements MCP server supporting multiple agent frameworks for seamless agent communication and coordination.
Enables Claude Desktop to interact with Hacker News for fetching news, comments, and user data via MCP protocol.
Integrates APIs, AI, and automation to enhance server and client functionalities dynamically.
Provides long-term memory for LLMs by storing and retrieving contextual information via MCP standards.
An advanced clinical evidence analysis server supporting precision medicine and oncology research with flexible search options.
A platform collecting A2A agents, tools, servers, and clients for effective agent communication and collaboration.
A Spring-based chatbot for Cloud Foundry that integrates with AI services, MCP, and memGPT for advanced capabilities.
An AI agent controlling macOS using OS-level tools, compatible with MCP, facilitating system management via AI.

Official Servers

A server setup enabling standardized exchange of model context information in digital services.
A minimal CLI tool to connect, interact, and communicate with MCP servers using command-line interface.
A collection of publicly available MCP servers for testing, development, and learning MCP implementation and interactions.
A client transport alternative for @modelcontextprotocol/sdk, optimized for React Native using sse.js for streaming.
A Node.js and TypeScript-based MCP server with Express.js, logging, environment config, testing, and Git integration.
A client to connect and interact with MCP servers, enabling tool discovery, authentication, and external service integration.
A server to interact with Asgardeo organization through LLM tools, enabling organization management automation.
A Python-based MCP client that generates UUIDs using OpenAI Agent and communicates with uuid-mcp-server.
A server designed to support Astro project development by providing runtime info, docs content, and integration data.
A sample MCP client demonstrating interaction with Weather and GitHub servers with limited tooling.