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🚀 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! 🎯
