MCP-AI Self-Learning API-to-cURL Model

A self-learning MCP server that converts API documentation into executable cURL commands.

python

1

GitHub Stars

python

Language

6 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "s-umasankar-api-to-curl-mcp-server": {
      "command": "/Users/umasankars/PycharmProjects/CapstoneMCPserver/venv/bin/python",
      "args": [
        "-m",
        "uvicorn",
        "src.mcp_server:app",
        "--reload"
      ]
    }
  }
}

You run an autonomous MCP server that translates API documentation into executable cURL commands. This enables you to quickly test and iterate against APIs by generating accurate request commands from described endpoints, improving developer efficiency and reducing manual curl crafting.

How to use

You interact with the MCP server using an MCP client. Provide the API documentation context to the server, and it will return corresponding cURL commands that you can execute against the target API. This workflow helps you explore endpoints, test requests, and validate responses without leaving your development environment.

Common usage patterns include submitting API descriptions or example requests and receiving ready-to-run cURL commands. You can then paste these commands into your terminal or automation scripts to execute and inspect results, logs, and error messages for rapid debugging.

How to install

Prerequisites: ensure you have Python installed (version 3.8+ is recommended) and pip available in your environment.

pip install -r requirements.txt

Start the MCP server so it can receive requests from your MCP client.

bash scripts/start_mcp.sh

Run the autonomous AI automation that powers the self-learning capabilities.

python src/ai_autonomous_dev.py

Optionally run tests to validate the system setup and basic flows.

pytest tests/

Troubleshooting and notes

If you encounter an issue where uvicorn is not found, install it or ensure your virtual environment is active.

pip install uvicorn

source /Users/umasankars/PycharmProjects/CapstoneMCPserver/venv/bin/activate
pip install -r requirements.txt

Available tools

dataset_generator

Automated dataset generation to create diverse API description inputs for model training and evaluation.

reinforcement_learner

Self-improving model component that applies reinforcement learning to refine command generation based on feedback from executed requests.

mcp_server_execution

MCP server module that exposes an API-based interface to accept documentation inputs and return generated cURL commands.

ci_cd_pipeline

Continuous deployment workflow using GitHub Actions to deploy updates and run automated tests.