MCP Server with Datasaur Sandbox

by ansemin

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# MCP Server with Datasaur Sandbox A comprehensive guide for beginners to set up and use a Model Context Protocol (MCP) server with Datasaur Sandbox. ## Table of Contents - [Introduction](#introduction) - [Prerequisites](#prerequisites) - [Installation](#installation) - [Configuration](#configuration) - [Running Your…

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- 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

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name MCP Server with Datasaur Sandbox

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

From the repository

Prerequisites

Before you begin, ensure you have:

1. Python 3.8+ installed on your system
- Download from python.org
- Make sure to check "Add Python to PATH" during installation

2. A Datasaur account with API access
- Sign up at datasaur.ai
- Obtain your API key from your account dashboard

3. Basic knowledge of command-line operations

Step 2: Set Up Your Environment

Create a virtual environment (recommended):

```bash

Here's the general pattern for creating a tool that accesses a Datasaur sandbox model:

```python
@mcp.tool()
async def call_your_model(prompt: str) -> str:
"""
Sends a given prompt string to your deployed model via Datasaur API sandbox.

Args:
prompt: The text prompt to send to the model.
"""
logging.debug(f"Received prompt: {prompt[:100]}...")

result = await client.call_tool("call_your_model", {"prompt": "Your prompt here"})
print(result)

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "mcp server with datasaur sandbox": {
            "MCP-Server---Datasaur": {
                "command": "python",
                "args": [
                    "-m",
                    "venv",
                    "venv"
                ]
            }
        }
    }
}

McpServers

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

A comprehensive guide for beginners to set up and use a Model Context Protocol (MCP) server with Datasaur Sandbox.

- Introduction - Prerequisites - Installation - Configuration - Running Your MCP Server - Using Your MCP Server - Troubleshooting - Extending Functionality

Introduction

What is MCP?

Model Context Protocol (MCP) is a standardized way for applications to communicate with AI models. It defines a protocol that allows tools to exchange structured data with large language models (LLMs), enabling them to use external functions and tools.

What is a Datasaur Sandbox?

Datasaur Sandbox provides managed API access to various AI models. This project implements an MCP server that acts as a bridge between your applications and Datasaur's API endpoints, allowing your applications to:

- Process and analyze data
- Access AI models deployed through Datasaur
- Build specialized assistants for various tasks

Prerequisites

Before you begin, ensure you have:

1. Python 3.8+ installed on your system
- Download from python.org
- Make sure to check "Add Python to PATH" during installation

2. A Datasaur account with API access
- Sign up at datasaur.ai
- Obtain your API key from your account dashboard

3. Basic knowledge of command-line operations

Installation

Step 1: Clone or Create the Project

Create a new directory for your project:

mkdir datasaur-mcp-server
cd datasaur-mcp-server

Step 2: Set Up Your Environment

Create a virtual environment (recommended):

```bash

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Setting up with Highlight

From the repository

Prerequisites

Step 2: Set Up Your Environment

Claude Desktop / Cursor

McpServers

Table of Contents

Introduction

What is MCP?

What is a Datasaur Sandbox?

Prerequisites

Installation

Step 1: Clone or Create the Project

Step 2: Set Up Your Environment

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