加载中
正在获取最新内容，请稍候...
Model Context Protocol Servers: Reference Implementations and Tools
Reference server implementations and tools for the Model Context Protocol, enabling standardized state and context management for AI/ML models.
Python
Added on 2025年6月15日53,752
Stars6,123
ForksPython
LanguageProject Introduction
Summary
This project contains official reference implementations and utility tools for building servers that communicate using the Model Context Protocol. It aims to provide robust, compliant examples to accelerate adoption and development within the ecosystem.
Problem Solved
The Model Context Protocol addresses the challenge of consistently managing and passing context (e.g., conversation history, user preferences, environmental state) to and from AI/ML models across different services and frameworks. This project provides concrete server implementations to make adopting the protocol easier.
Core Features
Protocol Compliance
Provides ready-to-use server implementations adhering to the Model Context Protocol specification.
Integration Examples
Includes examples and tooling for integrating various types of AI/ML models with the protocol servers.
Multi-language Support
Offers implementations in multiple programming languages to suit different development environments.
Tech Stack
Go
Python
gRPC
REST
Docker
Protocol Buffers
使用场景
The reference servers can be used in various scenarios where standardized context management for AI/ML models is crucial, including:
场景一：构建有状态的对话式AI系统
Details
Building conversational AI agents where managing turn-by-turn context and history across multiple model calls is essential for coherent interaction.
User Value
Ensures consistent and accurate model responses by providing necessary historical context.
场景二：集成多模态或多步AI工作流
Details
Integrating disparate AI services or models into a unified pipeline where context needs to be seamlessly passed between different processing stages.
User Value
Simplifies complex multi-model integrations through a standardized communication layer.
Recommended Projects
You might be interested in these projects
celerycelery
This project aims to simplify specific task processing flows through automation technology, significantly improving efficiency and accuracy. It is suitable for developers and analysts who need to handle large volumes of data.
Python
264004783
rcore-osrCore-Tutorial-v3
A comprehensive tutorial and codebase guiding you step-by-step to build a modern operating system kernel from scratch using Rust, targeting the RISC-V architecture. Ideal for learning OS principles and Rust systems programming.
Rust
1832516
dwylenglish-words
A large, plain text dictionary file containing over 479,000 English words, ideal for spell checkers, autocomplete, data analysis, and other word-based applications.
Python
112671920