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Mcpml

MCPML is a Python framework for building servers compliant with the Model Context Protocol (MCP), supporting CLI tools and OpenAI agent functions, and providing features such as dynamic loading and structured output.
Developer toolsArtificial intelligence chatbots#MCP framework#Python tool#AI agent#CLI support.Python
2.5 points
9.4K
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Overview

Content Details

Alternatives

What is MCPML?

MCPML is a Python framework specifically designed for building servers compliant with the Model Context Protocol (MCP). It allows developers to easily create AI services and interact with them through the Command Line Interface (CLI) or OpenAI Agent.

How to use MCPML?

After installation, you can run the MCP server through the command-line tool or integrate it into an existing Python project. Configuration is managed via YAML files, supporting both local and remote configurations.

Use cases

Suitable for scenarios that require standardized AI service interfaces, such as enterprise AI system integration, multi-model collaboration platform development, and AI applications that need to support both human and programmatic interactions.

Main features

MCP server framework
Provides a complete framework for building MCP-compatible servers, ensuring that services comply with the protocol standards
CLI tool integration
All server functions can be accessed via the command line, facilitating script integration and manual operation
OpenAI Agent support
Built-in support for OpenAI Agent, allowing tools to be implemented as AI agents
Dynamic loading
Supports dynamic loading of custom agent types and tool implementations from the execution directory
Structured output
Uses Pydantic models to support structured output, facilitating program processing
Advantages
Standardized interface: Follows the MCP protocol, ensuring service interoperability
Flexible deployment: Supports local and remote configurations
Multiple access methods: Supports both CLI and AI agent interactions
Easy to expand: The modular architecture facilitates the addition of new features
Limitations
Learning curve: Requires understanding of the basic concepts of the MCP protocol
Python dependency: Primarily targeted at the Python ecosystem
Initial configuration: Requires setting environment variables and configuration files

How to use

Installation
Install the MCPML package via pip
Configuration
Set environment variables (e.g., OpenAI API key) or create an.env file
Run the server
Start the server with default configuration
Use tools
Access specific tool functions via the CLI

Usage examples

Local development and testing
Developers quickly start a local MCP server for functional testing
Integrate custom tools
Integrate existing Python functions as MCP tools
Remote configuration deployment
Load remote configuration from a Git repository to start the service

Frequently Asked Questions

What is the difference between MCPML and ordinary Python server frameworks?
Is it necessary to use OpenAI services?
How to add custom tools?
Which transport protocols are supported?

Related resources

Official MCP protocol documentation
Specification of the Model Context Protocol
MCPML GitHub repository
Project source code and issue tracking
Pydantic documentation
Data validation library used for structured output

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

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