Python MCP Server Client
This project is a Python - based implementation of the MCP (Model Context Protocol) server and client, aiming to provide standardized interfaces for AI models, unify the tool call formats of different large - model vendors, and integrate document search functions for multiple AI frameworks.
rating : 2.5 points
downloads : 93
What is the MCP Server?
The MCP Server is a server that implements the Model Context Protocol (MCP), aiming to provide standardized interfaces for AI models to connect to external data sources and tools (such as file systems, databases, or APIs). It is equivalent to the 'USB - C interface' in the AI field, unifying the Function Call formats and tool encapsulation of different large - model vendors.How to use the MCP Server?
After installation via a simple command - line tool, you can configure the local Stdio protocol or remote SSE protocol service. It supports integration with clients such as Cursor and Cline, or direct calls through the Python client.Applicable Scenarios
1. Scenarios where multiple AI model tool calls need to be managed uniformly 2. Scenarios where AI agents need to access external documents and APIs during development 3. Projects for building a standardized AI tool ecosystemMain Features
Unified Tool Call InterfaceStandardize the Function Call formats of different AI vendors to solve the problem of inconsistent formats
Multi - Protocol SupportSupport two transmission protocols: Stdio (local) and SSE (remote)
Document Retrieval ToolBuilt - in support for retrieving content from mainstream AI framework documents, including LangChain, LlamaIndex, etc.
Multi - Client CompatibilitySupport popular AI development tools such as the Python native client, Cursor, and Cline
Advantages and Limitations
Advantages
Unified interface standard: Solve the problem of inconsistent Function Call formats of different AI vendors
Simplified tool encapsulation: Standardize the input and output formats of API tools
Flexible deployment: Support both local and cloud deployment modes
Ready - to - use: Built - in common document retrieval tools
Limitations
The Stdio protocol has specific requirements for the operating environment
Document retrieval depends on third - party APIs (such as Serper)
Requires Python environment support
How to Use
Environment Preparation
Install the UV package management tool and create a project
Install Dependencies
Activate the virtual environment and install necessary dependencies
Configure the Server
Create main.py and implement tool functions
Run the Server
Select the protocol type to start the service
Client Connection
Configure Cursor/Cline or use the Python client
Usage Examples
Retrieve LangChain DocumentsQuickly find official documents when developing LangChain applications
Build an AI Customer Service Knowledge BaseObtain the latest content from multiple knowledge bases in real - time through MCP
Frequently Asked Questions
Which API keys are required?
How to add custom tools?
Which clients are supported?
How to choose between the Stdio and SSE protocols?
Related Resources
MCP Official Documentation
Official documentation of the Model Context Protocol
Teaching Video
Tutorial video on building the MCP server
Serper API
Search API service
Cursor Documentation
Explanation of Cursor's support for MCP
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