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MCP Server with Datasaur Sandbox

@ansemin

代码仓库

基本信息

分类

推理

传输方式

stdio

作者

ansemin

关于 MCP Server with Datasaur Sandbox

暂无概览

#mcp-server#datasaur#ai-models

MCP Server with Datasaur Sandbox 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

MCP Server with Datasaur Sandbox 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

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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