MCP Directory

How to add Dingo to Cursor

AI data quality evaluation server exposing Dingo's rule- and LLM-based checks over MCP. Paste the config into ~/.cursor/mcp.json and restart Cursor.

Last updated June 14, 2026 · 718★ · stdio · apikey

Cursor config for Dingo

pip install dingo-python
{
  "mcpServers": {
    "dingo": {
      "command": "dingo",
      "args": [
        "serve",
        "--transport",
        "stdio"
      ],
      "env": {
        "OPENAI_API_KEY": "your-api-key",
        "OPENAI_BASE_URL": "https://api.openai.com/v1",
        "OPENAI_MODEL": "gpt-4"
      }
    }
  }
}

Setup steps

  1. 1Open Cursor → Settings → MCP → Add new MCP server (or edit ~/.cursor/mcp.json directly).
  2. 2Paste the Dingo config below into the "mcpServers" object.
  3. 3Fill in placeholder secrets, then save.
  4. 4Cursor reloads MCP servers automatically — check Settings → MCP for a green status dot.
  5. 5Ask Cursor to use one of Dingo's tools to confirm it's connected.

Before you start

  • Python with the dingo-python package installed (pip install dingo-python)
  • An LLM API key (e.g. OPENAI_API_KEY) for LLM-based evaluations; not needed for rule-based evaluations
  • An MCP client such as Cursor or Claude Desktop

What Dingo can do in Cursor

run_dingo_evaluation

Runs a rule-based or LLM-based Dingo evaluation on an input file or directory. Takes input_path, evaluation_type ('rule' or 'llm'), optional eval_group_name, output_dir, task_name, save_data/save_correct flags, and a kwargs dict (column mappings, data_format, dataset, custom_config with API keys, etc.). Returns the absolute path to the primary output file (e.g. summary.json).

list_dingo_components

Lists available Dingo rule groups, registered LLM model identifiers, and prompt definitions. Accepts component_type ('rule_groups', 'llm_models', 'prompts', or 'all') and include_details. Returns a dict of the requested component lists.

get_rule_details

Gets detailed information about a specific Dingo rule by rule_name, including its description, parameters, and evaluation characteristics.

get_llm_details

Gets detailed information about a specific Dingo LLM by llm_name, including its description, capabilities, and configuration parameters.

get_prompt_details

Gets detailed information about a specific Dingo prompt by prompt_name, including its description, associated metric type, and which groups it belongs to.

run_quick_evaluation

Runs a simplified evaluation from a high-level goal. Takes input_path and a natural-language evaluation_goal (e.g. 'check for inappropriate content', 'evaluate text quality'). Returns a summary of results or a path to the detailed results.

Security

LLM-based evaluations require an LLM API key. In stdio mode it is passed via env vars (e.g. OPENAI_API_KEY) in the client config; in SSE mode and for rule customization it is supplied via a custom_config JSON file. Rule-based evaluations need no key. The SSE transport runs an HTTP service (default http://localhost:8000/sse) that any local client can reach — bind host/port carefully if exposing beyond localhost.

Dingo + Cursor FAQ

Where is the Cursor config file?

Cursor reads MCP servers from ~/.cursor/mcp.json. Paste the Dingo config there under the "mcpServers" key and restart the client.

Is Dingo safe to use with Cursor?

LLM-based evaluations require an LLM API key. In stdio mode it is passed via env vars (e.g. OPENAI_API_KEY) in the client config; in SSE mode and for rule customization it is supplied via a custom_config JSON file. Rule-based evaluations need no key. The SSE transport runs an HTTP service (default http://localhost:8000/sse) that any local client can reach — bind host/port carefully if exposing beyond localhost.

Which transport should I use?

SSE is the default (run `dingo serve`, then point the client at http://localhost:8000/sse). stdio is for Claude Desktop or local agent spawn — the client launches `dingo serve --transport stdio` itself, so you don't start the server manually.

Do I need an API key?

Only for LLM-based evaluations. Rule-based evaluations run without any key. For LLM evals, provide the key via env vars (stdio mode) or a custom_config JSON file.

Do I need to clone the repo?

No. `pip install dingo-python` installs the `dingo` CLI which includes the MCP server. Cloning is only needed if you want to customize mcp_server.py directly.

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