🚀 MCP-AI: Self-Learning API-to-cURL Model
About
# 🚀 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…
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✅ Automated Dataset Generation
✅ Self-Improving Model with Reinforcement Learning
✅ MCP Server for API-based Execution
✅ Continuous Deployment with GitHub Actions
---
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
🚀 MCP-AI: Self-Learning API-to-cURL ModelCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
pip install -r requirements.txt
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"
)
---
pip install uvicorn
source /Users/umasankars/PycharmProjects/CapstoneMCPserver/venv/bin/activate
pip install -r requirements.txt
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"\ud83d\ude80 mcp-ai: self-learning api-to-curl model": {
"api-to-curl-mcp-server": {
"command": "python",
"args": [
"src/ai_autonomous_dev.py"
]
}
}
}
}McpServers
{
"api-to-curl-mcp-server": {
"command": "python",
"args": [
"src/ai_autonomous_dev.py"
]
}
}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
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