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Dingo MCP Server

@DataEval

Repository

Basic information

Category

Other

Transports

stdio

Author

DataEval

Submitted by

seanpjlab

About Dingo MCP Server

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

#data-science#data-quality

Dingo MCP Server config

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

{
  "mcpServers": {
    "dingo": {
      "command": "python",
      "args": [
        "mcp_server.py"
      ]
    }
  }
}

Dingo MCP Server 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 Dingo MCP Server?

Dingo MCP Server is a Python-based server that exposes Dingo evaluation capabilities through the Model Context Protocol (MCP). It allows developers to run rule-based and LLM-based data quality assessments directly from MCP-compatible clients like Cursor. This server is intended for users who need to evaluate datasets using Dingo’s built-in rule groups or custom LLM configurations.

How to use Dingo MCP Server?

Clone the repository, install dependencies (e.g., pip install -r requirements.txt), then run python mcp_server.py which starts the server via SSE transport by default. Customize the host, port, and log level inside the script’s mcp.run() call. Configure your MCP client (e.g., Cursor) by adding a "url" entry in its mcp.json that matches the server’s address.

Key features of Dingo MCP Server

  • 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.

Use cases of Dingo MCP Server

  • Evaluate local JSONL datasets using a predefined set of Dingo rules.
  • Perform LLM-based quality checks on text columns with a custom config.
  • Automate dataset evaluation directly from an AI coding assistant like Cursor.
  • Run batch scoring with configurable concurrency and output paths.

Frequently asked questions

1

What transport does Dingo MCP Server use by default?

It uses Server-Sent Events (SSE) as the default communication protocol, but this can be changed in `mcp.run()`.

2

How do I configure the server address for my MCP client?

In your client’s `mcp.json`, set the `"url"` to the server’s host and port (e.g., `http://127.0.0.1:8888/sse`) matching values from `mcp.run()`.

3

What are the prerequisites to run this server?

You need Git, Python 3.8+, and the `fastmcp` package. The `dingo` package must be importable from the cloned repository.

4

What if my data uses a column name other than 'content'?

Pass the column key via the `column_content` argument in the `kwargs` dictionary of `run_dingo_evaluation`.

5

How do I provide API keys for LLM evaluations?

API keys must be included inside the `custom_config` argument (as a file path, JSON string, or dictionary) when calling the LLM evaluation tool.

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