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

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dylibso/mcpx-py

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mcpx-py

PyPI

A Python library for interacting with LLMs using mcp.run tools

Features

AI Provider Support

Interactive Features

  • Real-time chat interface with AI models
  • Tool suggestion and execution within conversations
  • Support for both local and cloud-based AI providers

Dependencies

  • uv
  • npm
  • ollama (optional)

mcp.run Setup

You will need to get an mcp.run session ID by running:

npx --yes -p @dylibso/mcpx gen-session --write

This will generate a new session and write the session ID to a configuration file that can be used by mcpx-py.

If you need to store the session ID in an environment variable you can run gen-session without the --write flag:

npx --yes -p @dylibso/mcpx gen-session

which should output something like:

Login successful!
Session: kabA7w6qH58H7kKOQ5su4v3bX_CeFn4k.Y4l/s/9dQwkjv9r8t/xZFjsn2fkLzf+tkve89P1vKhQ

Then set the MPC_RUN_SESSION_ID environment variable:

$ export MCP_RUN_SESSION_ID=kabA7w6qH58H7kKOQ5su4v3bX_CeFn4k.Y4l/s/9dQwkjv9r8t/xZFjsn2fkLzf+tkve89P1vKhQ

Python Usage

Installation

Using uv:

uv add mcpx-py

Or pip:

pip install mcpx-py

Example code

from mcpx_py import Chat, Claude

llm = Chat(Claude)

# Or OpenAI
# from mcpx import OpenAI
# llm = Chat(OpenAI)

# Or Ollama
# from mcpx import Ollama
# llm = Chat(Ollama)

# Or Gemini
# from mcpx import Gemini
# llm = Chat(Gemini)

# Prompt claude and iterate over the results
async for response in llm.send_message(
    "summarize the contents of example.com"
):
    print(response)

More examples can be found in the examples/ directory

Command Line Usage

Installation

uv tool install mcpx-py

From git:

uv tool install git+https://github.com/dylibso/mcpx-py

Or from the root of the repo:

uv tool install .

uvx

mcpx-client can also be executed without being installed using uvx:

uvx --from git+https://github.com/dylibso/mcpx-py mcpx-client

Running

Get usage/help

mcpx-client --help

Chat with an LLM

mcpx-client chat

List tools

mcpx-client list

Call a tool

mcpx-client tool eval-js '{"code": "2+2"}'

LLM Configuration

Provider Setup

Claude
  1. Sign up for an Anthropic API account at https://console.anthropic.com
  2. Get your API key from the console
  3. Set the environment variable: ANTHROPIC_API_KEY=your_key_here
OpenAI
  1. Create an OpenAI account at https://platform.openai.com
  2. Generate an API key in your account settings
  3. Set the environment variable: OPENAI_API_KEY=your_key_here
Gemini
  1. Create an Gemini account at https://aistudio.google.com
  2. Generate an API key in your account settings
  3. Set the environment variable: GEMINI_API_KEY=your_key_here
Ollama
  1. Install Ollama from https://ollama.ai
  2. Pull your desired model: ollama pull llama3.2
  3. No API key needed - runs locally
Llamafile
  1. Download a Llamafile model from https://github.com/Mozilla-Ocho/llamafile/releases
  2. Make the file executable: chmod +x your-model.llamafile
  3. Run in JSON API mode: ./your-model.llamafile --json-api --host 127.0.0.1 --port 8080
  4. Use with the OpenAI provider pointing to http://localhost:8080