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Mcpx Py MCP Server

@dylibso

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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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点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

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

1

What dependencies are required?

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

2

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.

3

Which LLM providers are supported?

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

4

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`.

5

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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