sema4ai-mcp 0.0.3

Sema4AI Model Context Protocol (MCP)

Sema4AI Model Context Protocol (MCP) Library

The Model Context Protocol (MCP) is a protocol that AI agents can use to take action based on the output of a conversation with an LLM. This library provides an API that allows you to implement MCP tools, resources and prompts in your Python code.

It works a bit different from other MCP implementations in that the idea is that you'll just worry about the MCP tool/resource/prompt implementation in Python, but the actual management of the connection and lifecycle of your tool/resource/prompt functions is handled by the Sema4AI Action Server.

This means that you should just focus on the MCP functions, NOT on features provided by the Sema4AI Action Server such as:

• Running the server
• Making logging (which is automatically setup using robocorp.log)
• Creating your python environment (the environment is managed by a package.yaml file that's expected to be in the root of your project)
• Handling connections
• Providing a separate REST API to call your MCP functions (the regular REST API provided for actions in the Sema4AI Action Server may also be used to call your MCP functions)

Developing MCP functions:

Using the Sema4ai VSCode Extension, enables you to easily run your MCP functions locally and later on deploy as you'd like -- you can easily deploy your MCP server and agents to the cloud by using Sema4AI -- or just use the Sema4AI Action Server in a private cloud or locally as well.

Installation

pip install sema4ai-mcp

Usage

The package provides three main decorators:

@tool

Use the @tool decorator to define functions that can be used as tools by AI agents:

from sema4ai.mcp import tool

@tool
def assign_ticket(ticket_id: str, user_id: str) -> bool:
    """
    Assign a ticket to a user.

    Args:
        ticket_id: The ID of the ticket to assign.
        user_id: The ID of the user to assign the ticket to.

    Returns:
        True if the ticket was assigned successfully, False otherwise.
    """
    ...
    return True

The @tool decorator can also be used with the following arguments:

• title: A human-readable title for the tool.
• read_only_hint: If true, the tool does not modify its environment (default: False).
• destructive_hint: If true, the tool may perform destructive updates to its environment (default: True).
• idempotent_hint: If true, calling the tool repeatedly with the same arguments will have no additional effect on the its environment (default: False).
• open_world_hint: If true, this tool may interact with an "open world" of external entities (default: True). If False, the tool's domain of interaction is closed. For example, the world of a web search tool is open, whereas that of a memory tool is not.

@resource

Use the @resource decorator to define functions that provide resources to the LLM:

from sema4ai.mcp import resource

@resource("tickets://{ticket_id}")
def get_ticket(ticket_id: str) -> dict[str, str]:
    """
    Get ticket information from the database.

    Args:
        ticket_id: The ID of the ticket to get information for.

    Returns:
        A dictionary containing the ticket information.
    """
    return {"id": ticket_id, "summary": "This is a test ticket"}

@prompt

Use the @prompt decorator to define functions that generate prompts.

from sema4ai.mcp import prompt

@prompt
def make_a_summary(text: str) -> str:
    """
    Provide a prompt to the LLM.
    """
    return "Please make a summary of the following text: {text}"

License

This project is licensed under the Apache License 2.0.