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MCP-Go: Model Context Protocol Implementation in Go
A high-performance Go library implementing the Model Context Protocol (MCP), designed to seamlessly integrate Large Language Models (LLMs) with external data sources and tools, enhancing their capabilities.
Go
Added on 2025年6月19日5,888
Stars551
ForksGo
LanguageProject Introduction
Summary
MCP-Go is the official Go implementation of the Model Context Protocol (MCP). It acts as a bridge, allowing LLM applications to utilize external functions and access current data by providing a structured interface for tool and data source interaction.
Problem Solved
Connecting Large Language Models (LLMs) to real-time external data and tools is complex and often requires custom, ad-hoc solutions. The MCP-Go project solves this by providing a standardized, robust, and easy-to-use protocol implementation in Go, enabling reliable interaction between LLMs and the external environment.
Core Features
MCP Protocol Implementation
Provides a standard way for LLMs to interact with external functions and data, abstracting away complexity.
Go Native Performance
Built natively in Go for efficiency, concurrency, and easy integration into Go-based LLM applications or services.
Extensible Adapter System
Offers a framework for easily developing and registering adapters to connect to various external services (databases, APIs, etc.).
Tech Stack
Go
gRPC
Protocol Buffers
Concurrency Patterns
使用场景
The MCP-Go library can be applied in various scenarios where LLM applications need to interact with the outside world:
Enterprise Chatbots with Real-time Data Access
Details
Build enterprise chatbots that can fetch up-to-date information from internal databases or APIs (e.g., CRM, inventory) to answer user queries.
User Value
Enables chatbots to provide current, accurate information beyond their training data, increasing their utility in business contexts.
LLM-Powered Workflow Automation
Details
Develop LLM-powered agents that can perform actions like sending emails, creating calendar events, or updating records by calling external APIs defined as MCP tools.
User Value
Allows LLMs to be proactive agents, automating tasks and integrating with existing business processes.
Real-time Data Analysis & Monitoring
Details
Integrate LLMs into monitoring or data analysis pipelines where they need to query live system metrics or financial data for analysis or reporting.
User Value
Empowers LLMs to analyze and summarize real-time information streams, providing timely insights.
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