Dingo MCP Server

@DataEval

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

DataEval

提交者

seanpjlab

关于 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 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Dingo MCP Server 工具

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当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.

常见问题

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()`.

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()`.

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.

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`.

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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