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

Name: 🚀 MCP-AI: Self-Learning API-to-cURL Model
Author: S-Umasankar

@S-Umasankar

リポジトリ

🚀 MCP-AI: Self-Learning API-to-cURL Model について

概要はまだありません

基本情報

カテゴリ

開発者ツール

ランタイム

python

トランスポート

stdio

公開者

S-Umasankar

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "api-to-curl-mcp-server": {
      "command": "python",
      "args": [
        "src/ai_autonomous_dev.py"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is 🚀 MCP-AI: Self-Learning API-to-cURL Model?

This project builds an autonomous AI system that converts API documentation into cURL commands. It uses reinforcement learning to self-improve and is deployed via an MCP server with continuous integration through GitHub Actions.

How to use 🚀 MCP-AI: Self-Learning API-to-cURL Model?

Install dependencies with pip install -r requirements.txt, then start the MCP server by running bash scripts/start_mcp.sh. Launch the AI automation with python src/ai_autonomous_dev.py, and test the system with pytest tests/.

Key features of 🚀 MCP-AI: Self-Learning API-to-cURL Model

Automated dataset generation from API documentation
Self-improving model with reinforcement learning
MCP server for API-based execution
Continuous deployment with GitHub Actions
Packaged as an SDK via setup.py
Includes pre- and post-training scripts (pre_train.py, post_train.py)

Use cases of 🚀 MCP-AI: Self-Learning API-to-cURL Model

Automatically generating cURL commands from any REST API documentation
Rapid prototyping and testing of API endpoints
Training and deploying a dedicated AI model for API-to-cURL conversion
Integrating cURL generation into CI/CD pipelines via the MCP server

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

What are the main dependencies?

The setup.py requires FastAPI, Uvicorn, PyTorch, Transformers, sacrebleu, requests, pytest, and GitPython.

How do I start the MCP server?

Run bash scripts/start_mcp.sh. If uvicorn is not found, ensure it is installed (pip install uvicorn) and activate the Python virtual environment. Alternatively, modify the script to call python -m uvicorn.