# Protocol Standard

This project is an implementation of the Model Context Protocol (MCP) server, demonstrating how to build a functional server that can be integrated with multiple language model clients. As a standardized protocol, MCP provides a unified way for AI applications to connect data sources and tools, supporting three types of capabilities: resources, tools, and prompts.

awesome - mcp is a project that collects Model Context Protocol (MCP) servers/clients, aiming to simplify the integration of large language models with external systems through a standardized protocol.

This experiment guides users on how to deploy the MCP server and integrate it into Microsoft Copilot Studio, connecting AI models with data sources through a standardized protocol to achieve real-time data access and joke generation functions.

A modular server implementing the Model Context Protocol standard, providing integration of multiple tools such as GitHub, GitLab, Google Maps, memory storage, and Puppeteer web automation.

The Okta MCP Server is an innovative tool based on the Model Context Protocol (MCP) that enables AI models to directly interact with the Okta environment. It provides a standardized AI integration solution for IAM engineers and security teams, supporting core functions such as user management and group operations. It is currently in the Alpha testing phase.

MCP Tools is a custom server that implements the Model Context Protocol (MCP), providing file system and command execution tools for LLM clients such as Claude Desktop, and extending the local system interaction capabilities of LLMs.

The MCP Server is an interaction system based on the Model Context Protocol standard, combining IPFS distributed storage and Claude AI processing capabilities to achieve efficient interaction between models, agents, and contextual data.

The Model Context Protocol (MCP) is an open - source protocol that provides a standardized method for large language models to connect to external data sources and tools. It supports functions such as resources, prompts, tools, and sampling. This article focuses on how to develop and use MCP tool services.

The Akave MCP Server is a tool that enables interaction between AI models and Akave S3-compatible storage. It provides a standardized protocol to connect AI models with data sources, supports bucket and object management, signed URL generation, and other functions, and is compatible with Claude and local LLMs.

HubSpot has launched a beta version of the MCP Server, enabling AI clients to seamlessly operate HubSpot data and features, and supporting access through various MCP client configurations.

This is an example of an MCP server based on Spring Boot, used to interact with AI models and provide a standardized interface to access real - time data and services.

Awesome MCP Servers is a community - maintained curated collection of MCP servers that supports multiple programming languages and service types, aiming to expand AI capabilities.

Awesome MCP Servers is a curated list of Model Context Protocol (MCP) servers. MCP is an open protocol that enables AI models to securely interact with local and remote resources through standardized servers. This list focuses on production-ready and experimental MCP servers that extend AI capabilities through file access, database connections, API integration, and other context services.

The AWS MCP Servers are a set of dedicated servers based on the Model Context Protocol, offering various AWS-related functions, including document retrieval, knowledge base query, CDK best practices, cost analysis, image generation, etc., aiming to enhance the integration of AI applications with AWS services through a standardized protocol.

This project is a collection of various Model Context Protocol (MCP) servers used to extend the capabilities of AI assistants, supporting the direct use of external tools and services in Claude conversations.

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 demonstrates an example implementation of the Model Context Protocol (MCP), which is a standardized protocol for enhancing the capabilities of AI assistants and extending their functionality through custom tools and data sources. The project includes multiple MCP server examples, such as task managers, file explorers, and weather services, and provides guidelines on how to set up and use these servers.

This project provides multiple Model Context Protocol (MCP) services for extending AI assistant capabilities and supports standalone installation and use.

The Model Context Protocol (MCP) is a standardized protocol for seamless communication between development environments (such as Cursor) and AI models, providing structured context sharing and AI capabilities integration.

The official website of A2A-MCP showcases the technical vision, core advantages, and application scenarios of the multi-agent collaboration protocol, and is built using a modern front-end technology stack.