Confidential guide on numerology and astrology, based of GG33 Public information

MCPML
MCP Server Markup Language (MCPML) - Ein Python -Framework zum Erstellen von MCP -Servern mit CLI und OpenAI -Agentenunterstützung.
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MCP Server Markup Language (MCPML)
A Python framework for building Model Context Protocol (MCP) servers with CLI and OpenAI Agent support.
Features
- 🚀 MCP Server Framework: Build MCP-compliant servers in Python
- 🔧 CLI Tools: All server capabilities exposed as CLI commands (to be consumed by humans or scripts rather than MCP clients)
- 🤖 OpenAI Agent SDK Support: Implement tools as OpenAI agents Or as simple python functions
- 🔄 Agent-to-MCP Integration: Agents can consume MCP services via config
- 🛠️ Extensible Architecture: Easily add custom tools and services
- 🔌 Dynamic Loading: Support for custom agent types and tool implementations from the execution directory
- 📦 Structured Output: Support for structured output using Pydantic models
Installation
pip install git+https://github.com/a5c-ai/mcpml#egg=mcpml
.env
OPENAI_API_KEY=your_openai_api_key
or
AZURE_OPENAI_ENDPOINT=https://your-azure-openai-endpoint.openai.azure.com
AZURE_OPENAI_API_KEY=your_azure_openai_api_key
OPENAI_API_VERSION=api_version
Usage
mcpml --help
mcpml run
mcpml.yaml is the default config file for the MCPML server.
mcpml run -c https://github.com/a5c-ai/some-mcpml-server
mcpml -c mcpml.yaml tools some-tool run --arg1 value1 --arg2 value2
using uvx:
uvx --from git+https://github.com/a5c-ai/mcpml#egg=mcpml mcpml -c mcpml.yaml tools list
uvx --from git+https://github.com/a5c-ai/mcpml#egg=mcpml mcpmp run --transport=sse
License
MIT
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Reviews

user_BHRV1YSA
As a loyal user of MCP applications, I must say that MCPML by a5c-ai is a fantastic tool! Its user-friendly interface and powerful machine learning capabilities make data analysis and prediction tasks a breeze. I highly recommend MCPML to anyone looking to streamline their ML processes. You can check it out on GitHub at https://github.com/a5c-ai/mcpml.