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

@S-Umasankar

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基本情報

カテゴリ

開発者ツール

トランスポート

stdio

作者

S-Umasankar

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

概要はまだありません

MCP-AI: Self-Learning API-to-cURL Model の設定

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"
      ]
    }
  }
}

MCP-AI: Self-Learning API-to-cURL Model のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

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

よくある質問

1

What are the main dependencies?

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

2

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`.

3

How can I test the system?

Use `pytest tests/` after installing dependencies and starting the server.

4

What is the role of the MCP server?

It provides an API-based interface to execute the cURL generation model, enabling integration with other tools and workflows.

5

Does the server require authentication or configure a specific transport?

The README does not mention any authentication mechanism or transport configuration beyond the default Uvicorn HTTP server.

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