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MCP Server with Datasaur Sandbox

@ansemin

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基本情報

カテゴリ

推論

トランスポート

stdio

作者

ansemin

MCP Server with Datasaur Sandbox について

概要はまだありません

#mcp-server#datasaur#ai-models

MCP Server with Datasaur Sandbox 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": {
    "MCP-Server---Datasaur": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

MCP Server with Datasaur Sandbox 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 Server with Datasaur Sandbox?

This server implements a Model Context Protocol (MCP) bridge between your applications and Datasaur’s API endpoints, allowing you to process data, access AI models deployed through Datasaur, and build specialized assistants. It is designed for beginners with step‑by‑step setup instructions.

How to use MCP Server with Datasaur Sandbox?

Install Python 3.8+, clone the project, create a virtual environment, install dependencies (mcp[cli], httpx, python‑dotenv, uv), create a .env file with DATASAUR_API_KEY and DATASAUR_SANDBOX_API_URL, and then run python main.py. For Claude Desktop, configure claude_desktop_config.json with the server command and environment variables.

Key features of MCP Server with Datasaur Sandbox

  • Bridges applications with Datasaur’s managed AI model APIs
  • Provides data processing tools (e.g., CSV to JSON conversion)
  • Enables prompting any AI model deployed in Datasaur
  • Supports creation of helper tools for common tasks
  • Includes troubleshooting guidance and extension patterns

Use cases of MCP Server with Datasaur Sandbox

  • Process and analyze structured data through AI models
  • Access language models, code assistants, or domain‑specific experts deployed on Datasaur
  • Build specialized assistants for tasks like email drafting or report generation
  • Integrate Datasaur’s AI capabilities into custom applications via MCP protocol

よくある質問

1

What prerequisites are needed?

Python 3.8+, a Datasaur account with API access (obtain API key from dashboard), and basic command‑line knowledge.

2

How do I get my Datasaur API URL?

Log into Datasaur, go to the Deployments section, create or select a deployment, and note your deployment ID and model ID. The URL format is `https://deployment.datasaur.ai/api/deployment/{deployment_id}/{model_id}/chat/completions`.

3

Where are API keys stored and what transport is used?

API keys are stored in a `.env` file (not committed) and passed via environment variables. The default transport is `stdio`, as shown in the Claude Desktop configuration example.

4

What should I do if I get an “API key not configured” error?

Ensure your `.env` file includes `DATASAUR_API_KEY` and that the key is correctly copied from your Datasaur account.

5

Can I add more models from Datasaur?

Yes. Add the new URL as an environment variable, update your configuration, and create an additional tool function in `main.py` following the provided pattern.

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