What is Mcpx Py

mcpx-py is a Python client library designed for interacting with AI models using tools from mcp.run, enabling developers to build applications that leverage large language models (LLMs).

Use cases

Use cases for mcpx-py include creating chatbots that summarize content, generating text based on prompts, and developing applications that require interaction with LLMs.

How to use

To use mcpx-py, install it via ‘uv add mcpx-py’ or ‘pip install mcpx-py’. Set up an mcp.run session ID using ‘npx --yes -p @dylibso/mcpx gen-session --write’, and then use the library to send messages to AI models.

Key features

Key features of mcpx-py include support for various AI models, structured output capabilities, and easy integration with mcp.run tools.

Where to use

mcpx-py can be used in fields such as AI development, chatbots, content generation, and any application that requires natural language processing.

mcpx-py

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

Features

AI Provider Support

mcpx-py supports all models supported by PydanticAI

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

llm = Chat("claude-3-5-sonnet-latest")

# Or OpenAI
# llm = Chat("gpt-4o")

# Or Ollama
# llm = Chat("ollama:qwen2.5")

# Or Gemini
# llm = Chat("gemini-2.0-flash")

response = llm.send_message_sync(
    "summarize the contents of example.com"
)
print(response.data)

It’s also possible to get structured output by setting result_type

from mcpx_py import Chat, BaseModel, Field
from typing import List

class Summary(BaseModel):
    """
    A summary of some longer text
    """
    source: str = Field("The source of the original_text")
    original_text: str = Field("The original text to be summarized")
    items: List[str] = Field("A list of summary points")

llm = Chat("claude-3-5-sonnet-latest", result_type=Summary)
response = llm.send_message_sync(
    "summarize the contents of example.com"
)
print(response.data)

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 mcpx-py mcpx-client

Or from git:

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