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MCP Server with Datasaur Sandbox

@ansemin

Repository

About MCP Server with Datasaur Sandbox

No overview available yet

#mcp-server#datasaur#ai-models

Basic information

Category

Reasoning

Runtime

python

Transports

stdio

Publisher

ansemin

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

Comments

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