axiom-mcp 0.1.4

🚀 MCP framework that unlocks truly scalable AI systems with zero friction

Axiom MCP

🚀 Robust and Dev friendly MCP framework

NOTE

This will be oss very soon, working on docs + other misc stuff, if you want to contribute, send me an email.

Installation

Using uv (recommended):

uv pip install axiom-mcp

Development Setup

1. Clone the repository:

   git clone https://github.com/axiomml/axiom-mcp.git
   cd axiom-mcp
   

2. Install uv if you haven't already:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

3. Create and activate a virtual environment with uv:

   uv venv --python 3.12
   source .venv/bin/activate  # On Unix/Linux
   # or
   .venv\Scripts\activate  # On Windows
   

4. Install development dependencies:

   uv sync --frozen --extra dev
   

Core Features

1. Tool Definition

Tools in Axiom MCP are defined as classes that inherit from the Tool base class. Here's how to define a tool:

from axiom_mcp.tools.base import Tool, ToolMetadata, ToolValidation

# Define input schema for tool validation
number_input_schema = {
    "type": "object",
    "properties": {
        "a": {"type": "number", "description": "First number"},
        "b": {"type": "number", "description": "Second number"},
    },
    "required": ["a", "b"],
}

class AddTool(Tool):
    """Tool for adding two numbers."""
    metadata = ToolMetadata(
        name="add",
        description="Add two numbers together",
        validation=ToolValidation(input_schema=number_input_schema),
        author="MathServer",
        version="1.0.0",
    )

    async def execute(self, args: Dict[str, Any]) -> Dict[str, Any]:
        a, b = args["a"], args["b"]
        result = a + b
        return {
            "type": "text",
            "content": {"operation": "addition", "a": a, "b": b, "result": result},
        }

2. Tool Logging and Metrics

Axiom MCP provides comprehensive logging and metrics capabilities for tools:

Basic Logging

Each tool has access to a context with built-in logging methods:

class MyTool(Tool):
    async def execute(self, args: Dict[str, Any]) -> Dict[str, Any]:
        # Access logging through tool context
        self.context.debug("Debug level message")
        self.context.info("Processing started")
        self.context.warning("Warning message")
        self.context.error("Error occurred")

        # Your tool logic here
        return result

Metrics Tracking

The tool manager automatically tracks:

• Total calls
• Successful calls
• Failed calls
• Average execution time
• Last used timestamp

Metrics are stored in the logs/tools.log file by default and include:

• Tool name
• Operation status
• Timestamp
• Execution time
• Error messages (if any)

Advanced Features

1. Cache Control:

# Disable caching for specific execution
tool_context = ToolContext(cache_enabled=False)
tool = MyTool(context=tool_context)

2. Execution Timeout:

# Set custom timeout
tool_context = ToolContext(timeout=30.0)  # 30 seconds

3. Dry Run Mode:

# Enable dry run for testing
tool_context = ToolContext(dry_run=True)

3. Resource Definition

Resources are lightweight endpoints that can be defined using decorators:

from pathlib import Path
from axiom_mcp import AxiomMCP

mcp = AxiomMCP("MyServer", port=8888)

# Simple string resource
@mcp.resource("greeting://{name}")
def say_hello(name: str) -> str:
    return f"Hello, {name}!"

# Resource returning a list of files
@mcp.resource("dir://desktop")
def list_files() -> list[str]:
    desktop = Path.home() / "Documents"
    return [str(f) for f in desktop.iterdir()]

Running the Server

axiom-mcp dev server.py # This runs in dev mode
axiom-mcp run server.py # This runs in release/prod mode

Development Commands

# Run tests
uv run pytest

# Run tests with coverage
uv run pytest tests

# Update dependencies
uv add <dep>

# Sync your environment
uv sync

Contributing

1. Fork the repository
2. Create a new branch for your feature
3. Make your changes
4. Run the tests:

   uv run pytest
   

5. Submit a pull request

License

GNU General Public License v3 (GPLv3)