iae automcp

    2

    Autonomous AI skill that transforms OpenAPI/Swagger specs, Postman collections, and API endpoints into validated FastMCP Python servers with automated quality checks and error-fixing loops.

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    iae automcp

    Example session with this skill installed

    Convert this weather API spec into a FastMCP server: [{"name": "get_temp", "description": "Get city temp", "method": "GET", "path": "/temp", "parameters": [{"name": "city", "type": "string"}]}]

    • Read your context and instructions
    • Compiled the iae automcp

    I have generated and validated a FastMCP server based on your weather API specification. The server passed all compliance checks with a score of 95/100, including AST syntax and Ruff linting.

    Generated FastMCP Server

    from mcp.server.fastmcp import FastMCP
import httpx
from pydantic import Field

# Initialized AutoGeneratedService
mcp = FastMCP("WeatherService")

@mcp.tool()
async def get_temp(city: str = Field(..., description="City name")) -> str:
    """Get city temp"""
    async with httpx.AsyncClient() as client:
        # Requesting data from the weather endpoint
        response = await client.get(f"https://api.example.com/temp?city={city}")
        response.raise_for_status()
        return response.text

if __name__ == "__main__":
    mcp.run()

    Compliance Report

    • AST Syntax: PASS
    • Ruff Linting: PASS (0 errors)
    • MCP Protocol Check: PASS
    • Parameter Validation: PASS

    Next steps

    1. Install dependencies using pip install fastmcp httpx ruff.
    2. Run the server locally using python weather_service.py to test the connection.
    3. Add your specific API base URL and authentication headers to the httpx.AsyncClient call.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Manual conversion of API specifications into Model Context Protocol (MCP) servers is repetitive and error-prone. This skill automates the entire pipeline, transforming OpenAPI schemas, Postman collections, or raw endpoint lists into production-ready FastMCP Python servers. It eliminates the manual overhead of mapping parameters and ensures the resulting code follows strict protocol standards.

    What it does

    • FastMCP generation translates complex API schemas and SDK specifications into clean, runnable Python scripts.
    • Multi-layer compliance checking performs Python AST syntax validation and MCP protocol structure verification.
    • Static analysis runs Ruff linting to ensure code quality and PEP 8 compliance.
    • Iterative self-healing detects errors or low compliance scores and automatically patches the server code until it passes quality gates.
    • Runtime safety verification checks for execution risks before the server is deployed.

    How it works

    1. Ingest specification by providing a path to an openapi.json, swagger.json, or Postman collection file.
    2. Execute generator using the generate_mcp.py script to map endpoints to MCP tools.
    3. Run validator to trigger the 4-layer inspector and generate a quality score.
    4. Iterate automatically if the compliance score falls below the 80% threshold.

    Frameworks & tools

    Python 3.10+, FastMCP, Uvicorn, Httpx, Pydantic, Ruff, and Google GenAI.

    Why this beats prompting it yourself

    Standard prompting often misses strict MCP protocol nuances or fails to correctly type-hint Pydantic models for complex nested schemas. This skill uses a dedicated self-healing loop and static analysis to ensure the generated server is actually valid and runnable, rather than just looking correct.

    Use cases

    • Legacy API wrapping to quickly expose internal services to AI agents.
    • SaaS integration for building MCP connectors to third-party tools like Stripe or HubSpot.
    • Quality enforcement to fix existing MCP servers that fail protocol validation.

    Known limitations

    Requires Python 3.10 or higher. The iterative healing process requires an active Google GenAI connection for logic correction.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Trust & safety

    Security scanned

    Verified clean 4 days ago

    • Free to download with an account

    Requires

    Python

    Listed4 days ago

    What's inside

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