Advertise
MCP.so
Sign In

MCP Server with Datasaur Sandbox

@ansemin

Repository

Basic information

Category

Reasoning

Transports

stdio

Author

ansemin

About MCP Server with Datasaur Sandbox

No overview available yet

#mcp-server#datasaur#ai-models

MCP Server with Datasaur Sandbox config

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

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

MCP Server with Datasaur Sandbox tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

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

Frequently asked questions

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.

Comments

More Reasoning MCP servers

Task Planner MCP Server

CaptainCrouton89

An MCP (Model Context Protocol) server that helps AI assistants (like Claude) break down complex tasks into manageable steps, track progress, and manage a hierarchical task list.

13

MCP Servers

junfanz1

This project demonstrates a decoupled real-time agent architecture that connects LangGraph agents to remote tools served by custom MCP (Modular Command Protocol) servers. The architecture enables a flexible and scalable multi-agent system where each tool can be hosted independent

27

Agentic Tools MCP Server

Pimzino

A comprehensive Model Context Protocol (MCP) server providing AI assistants with powerful task management and agent memories capabilities with project-specific storage.

87

Emergency Medicare Management MCP Server

manolaz

emergency-medicare-planner-mcp-server

5

mcp-agentic-rag

rukshannet

MCP Server for Agentic RAG applications

4
Advertise