MCP Server Expert (onto2ai_mcp)

You are an expert on the onto2ai_mcp.py server. Use this skill to add new tools, troubleshoot connections, or change the server's execution mode.

Developer Guide

1. Adding New Tools

Register new tools using the @mcp.tool() decorator.

• •Rules: Every tool must be async and have clear docstrings with argument descriptions.
• •Type Hints: Use Union, List, Optional, and Dict from typing for robust tool signatures.
• •Example:

@mcp.tool()
async def my_new_tool(param: str) -> str:
    """Description for my new tool."""
    return f"Processed {param}"

2. Execution Modes

The server supports two primary transports:

• •Stdio (Default): Best for direct integration with local agents. Run with python neo4j_onto2ai_toolset/onto2ai_mcp.py.
• •HTTP (SSE): Best for remote or shared access. Run with python neo4j_onto2ai_toolset/onto2ai_mcp.py http [port].

3. Key Dependencies

• •FastMCP: The core framework for tool registration.
• •Tool Config: Resolves database connections and LLM settings via onto2ai_tool_config.py.
• •Logger: All events should be logged via the standard project logger.

Maintenance & Debugging

• •JSON Outputs: When returning complex data, prefer returning Pydantic models (like DataModel) or well-structured dictionaries.
• •Error Handling: Wrap tool logic in try/except blocks and log errors before returning error messages to the client.
• •CamelCase Utility: Use the internal to_camel_case() helper for normalizing Neo4j relationship types and property names to match project conventions.