What is Api To Curl Mcp Server

api-to-curl-mcp-server is an autonomous AI system designed to convert API documentation into cURL commands, facilitating seamless interaction with APIs.

Use cases

Use cases include automating API calls for testing purposes, generating cURL commands from API documentation for developers, and enhancing API interaction workflows in DevOps processes.

How to use

To use api-to-curl-mcp-server, install the necessary dependencies using ‘pip install -r requirements.txt’, start the MCP Server with ‘bash scripts/start_mcp.sh’, initiate AI automation by running ‘python src/ai_autonomous_dev.py’, and test the system using ‘pytest tests/’.

Key features

Key features include automated dataset generation, a self-improving model utilizing reinforcement learning, an MCP Server for API-based execution, and continuous deployment capabilities with GitHub Actions.

Where to use

api-to-curl-mcp-server can be used in software development, API testing, and automation environments where efficient API interaction is required.

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

This project builds an autonomous AI system to convert API documentation into cURL commands.

📌 Features:

✅ Automated Dataset Generation
✅ Self-Improving Model with Reinforcement Learning
✅ MCP Server for API-based Execution
✅ Continuous Deployment with GitHub Actions

🚀 Quick Start:

1️⃣ Install dependencies:

pip install -r requirements.txt

2️⃣ Start MCP Server:

bash scripts/start_mcp.sh

3️⃣ Start AI Automation:

python src/ai_autonomous_dev.py

4️⃣ Test System:

pytest tests/

📜 setup.py (For Packaging SDK)

from setuptools import setup, find_packages

setup(
    name="mcp_sdk",
    version="1.0",
    packages=find_packages(),
    install_requires=[
        "fastapi",
        "uvicorn",
        "torch",
        "transformers",
        "sacrebleu",
        "requests",
        "pytest",
        "gitpython",
    ],
    author="Your Name",
    description="MCP SDK for API-to-cURL Model Automation",
    license="MIT"
)

✅ Final Steps

1️⃣ Install dependencies

pip install -r requirements.txt

2️⃣ Start MCP Server

bash scripts/start_mcp.sh

3️⃣ Run AI Automation

python src/ai_autonomous_dev.py

4️⃣ Test System

pytest tests/

Fix uvicorn: command not found

The error indicates that uvicorn is not installed or not in the system path.

✅ Solution 1: Install Uvicorn

pip install uvicorn

✅ Solution 2: Ensure Virtual Environment is Activated

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

✅ Solution 3: Explicitly Call Python for Uvicorn

Modify scripts/start_mcp.sh to:

#!/bin/bash
echo "🚀 Starting MCP Server..."
/Users/umasankars/PycharmProjects/CapstoneMCPserver/venv/bin/python -m uvicorn src.mcp_server:app --reload

Final Steps

After applying the fixes, restart everything:

pip install --upgrade pip setuptools wheel
pip install -r requirements.txt
bash scripts/start_mcp.sh

🚀 Now the system is fully organized and self-learning! 🎯