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The MCP Python SDK is a Python library that implements the Model Context Protocol (MCP), used to standardize the interaction between LLM applications and servers, supporting the exposure and management of resources, tools, and prompts.
rating : 2.5 points
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What is an MCP server?
An MCP server is a standardized interface service specifically designed for large language models (LLMs), which can securely expose data resources, functional tools, and interaction templates. It is similar to an API gateway designed for AI.How to use an MCP server?
By simply defining resources and tools using Python decorators, you can create services that can directly interact with AI applications such as Claude. It supports quick installation on the desktop environment or integration into existing systems.Applicable scenarios
Scenarios that require secure interaction between LLMs and systems, such as enterprise knowledge base access, data analysis tool integration, automated workflow triggering, and customized AI interaction templates.Core features
Data resource exposureSecurely provide structured data through URL patterns, such as database content, API responses, or file information
Functional tool integrationConvert Python functions into tools that can be called by AI, supporting parameter validation and type safety
Interaction templatesCreate reusable conversation templates to standardize the interaction between AI and the system
Lifecycle managementBuilt-in resource initialization and cleanup mechanisms to ensure service reliability
Advantages and limitations
Advantages
Standardized interface: Expose various resources and functions in a unified way
Developer-friendly: Python decorators simplify service creation
Secure and controllable: Clear permissions and access boundaries
Plug-and-play: Support quick integration into AI applications such as Claude
Limitations
Learning curve: Need to understand the concepts of the MCP protocol
Performance overhead: The protocol layer adds a small amount of latency
Function limitations: Mainly targeted at LLM interaction scenarios
Usage guide
Install the SDK
Install the Python package using pip or uv
Create a service file
Create a new Python file and define resources and tools using decorators
Run the service
Test in development mode or install in the production environment
Application cases
Enterprise knowledge base queryExpose the internal document system through MCP, allowing AI to directly answer employees' questions
Data analysis toolExpose the SQL query tool to AI to automatically generate data insights
Frequently Asked Questions
Does the MCP service need to run continuously?
How to control AI access permissions?
Which AI clients are supported?
Learning resources
Official documentation
Complete protocol specifications and API references
GitHub repository
Source code and issue tracking
Example projects
Implementation examples for various application scenarios
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