MCP Server with Datasaur Sandbox

Name: MCP Server with Datasaur Sandbox
Author: ansemin

@ansemin

リポジトリ

MCP Server with Datasaur Sandbox について

概要はまだありません

#mcp-server #datasaur #ai-models

基本情報

カテゴリ

推論

ランタイム

python

トランスポート

stdio

公開者

ansemin

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"
      ]
    }
  }
}

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 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

FAQ from MCP Server with Datasaur Sandbox

What prerequisites are needed?

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

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.

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.

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.

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.