Mcpx Py MCP Server

@dylibso

代码仓库主页

基本信息

分类

其他

许可证

BSD-3-Clause

传输方式

stdio

作者

dylibso

关于 Mcpx Py MCP Server

Python client library for https://mcp.run - call portable & secure tools for your AI Agents and Apps

#python #ai #mcp #agents #llm #mcp-server #mcp-client#mcp-run

Mcpx Py MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcpx-py": {
      "command": "npx",
      "args": [
        "--yes",
        "-p",
        "@dylibso/mcpx",
        "gen-session",
        "--write"
      ]
    }
  }
}

Mcpx Py MCP Server 工具

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概览

What is mcpx-py?

mcpx-py is a Python library for interacting with large language models (LLMs) using tools provided by mcp.run. It supports all models supported by PydanticAI and offers both a Python API and a command-line interface.

How to use mcpx-py?

Install the library via uv add mcpx-py or pip install mcpx-py. Obtain an mcp.run session ID by running npx --yes -p @dylibso/mcpx gen-session --write and set the MCP_RUN_SESSION_ID environment variable. Then import mcpx_py to use the Chat class or invoke the mcpx-client command-line tool.

Key features of mcpx-py

Python library for LLM interaction via mcp.run tools.
Supports all models supported by PydanticAI.
Structured output with Pydantic models.
Command line interface for chat, list, and tool calls.
Works with Anthropic, OpenAI, Gemini, Ollama, and Llamafile.

Use cases of mcpx-py

Summarize web content using an LLM.
Call remote tools via mcp.run from Python or CLI.
Build AI applications with structured data outputs.
Experiment with multiple LLM providers.

常见问题

What dependencies are required?

You need `uv`, `npm`, and optionally `ollama` for local models.

How do I set up authentication?

Generate an mcp.run session ID using `npx --yes -p @dylibso/mcpx gen-session` and either write it to a config file (with `--write`) or set the `MCP_RUN_SESSION_ID` environment variable.

Which LLM providers are supported?

The library supports Claude, OpenAI, Gemini, Ollama, Llamafile, and any model supported by PydanticAI.

How do I get structured output from the LLM?

Pass a Pydantic `BaseModel` subclass as the `result_type` parameter when creating a `Chat` instance, then call `send_message_sync`.

Can I use mcpx-py without installing it permanently?

Yes, you can run `mcpx-client` directly using `uvx --from mcpx-py mcpx-client` without permanent installation.

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