๐ MCP framework that unlocks truly scalable AI systems with zero friction
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# Axiom MCP ๐ 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): ```bash uv pip install axiom-mcp ``` ## Development Setup 1. Clone the repository: ```bash git clone https://github.com/axiomml/axiom-mcp.git cd axiom-mcp ``` 2. Install uv if you haven't already: ```bash curl -LsSf https://astral.sh/uv/install.sh | sh ``` 3. Create and activate a virtual environment with uv: ```bash uv venv --python 3.12 source .venv/bin/activate # On Unix/Linux # or .venv\Scripts\activate # On Windows ``` 4. Install development dependencies: ```bash 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: ```python 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 โฆ
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