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()`.
D
Dingo MCP Server
@DataEval
基本信息
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
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
工具列表暂未提供
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
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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