Dingo MCP Server
About
MCP server for the Dingo: a comprehensive data quality evaluation tool. Server enables interaction with Dingo's rule-based and LLM-based evaluation capabilities and rules, and prompts listing. Official GitHub link: https://github.com/DataEval/dingo
Explore
- Lists available Dingo rule groups and LLM model identifiers.
- Runs rule-based evaluations with configurable rule groups.
- Runs LLM-based evaluations with custom configuration files.
- Supports local, Hugging Face, and other dataset inputs.
- Allows saving detailed outputs (JSONL, correct data) to disk.
- Integrates seamlessly with Cursor’s MCP system.
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
Dingo MCP ServerCommand (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
From the repository
1. Prerequisites: Ensure you have Git and a Python environment (e.g., 3.8+) set up.
2. Clone the Repository: Clone this repository to your local machine.
git clone https://github.com/DataEval/dingo.git
cd dingo
3. Install Dependencies: Install the required dependencies, including FastMCP and other Dingo requirements. It's recommended to use the
requirements.txt file. pip install -r requirements.txt
4. Ensure Dingo is Importable: Make sure your Python environment can find the
dingo package within the cloned repository when you run the server script.
Navigate to the directory containing mcp_server.py and run it using Python:
python mcp_server.py
By default, the server starts using the Server-Sent Events (SSE) transport protocol. You can customize its behavior using arguments within the script's mcp.run() call:
```python
Once configured, you can invoke the Dingo tools within Cursor:
List Components: "Use the dingo_evaluator tool to list available Dingo components."
Run Evaluation: "Use the dingo_evaluator tool to run a rule evaluation..." or "Use the dingo_evaluator tool to run an LLM evaluation..."
Cursor will prompt you for the necessary arguments.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"dingo mcp server": {
"dingo": {
"command": "python",
"args": [
"mcp_server.py"
]
}
}
}
}McpServers
{
"dingo": {
"command": "python",
"args": [
"mcp_server.py"
]
}
}list_dingo_components()
Lists available Dingo rule groups and registered LLM model identifiers.
Arguments: None
Returns: Dict[str, List[str]] - A dictionary containing rule_groups and llm_models.
Example Cursor Usage:
> Use the dingo_evaluator tool to list dingo components.
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