dashai-mcp

An MCP server for dashAI, the open source Machine Learning workbench led by the University of Chile (FCFM), built by students of DCC UChile and UTFSM, with CENIA and IMFD.

Unofficial and independent. This is a third-party project. It is not affiliated with, endorsed by, or maintained by the dashAI project or the institutions that develop it.

It gives an agent the same surface dashAI gives a person through its GUI: look at datasets, see which models are available, train, follow queued work, and read the metrics.

"Train a random forest on dataset 3 predicting 'species' and tell me the F1"

Status

v0.2.1 — verified against a running dashAI 0.9.7.post1. Deterministic tests (including the predict two-step path) plus a live run: dashai_train_model → dashai_job_status → dashai_get_run with metrics, and dashai_predict → dashai_job_status finished.

Verifying against a live instance surfaced gaps between dashAI's documentation and its actual behaviour. Each one has its own regression test. A fifth — dashai_predict sending run_id to PredictJob — only showed up live (KeyError: 'prediction_id') because there was no predict test.

Install

uv pip install git+https://github.com/Maarmapa/dashai-mcp
# or: pip install git+https://github.com/Maarmapa/dashai-mcp

Not published on PyPI yet.

In your MCP client configuration:

{
  "mcpServers": {
    "dashai": {
      "command": "dashai-mcp"
    }
  }
}

dashAI has to be running separately (dashai, or the desktop app). It is looked up at http://localhost:8000 by default.

Tools

Five things dashAI's documentation gets wrong

Found by running against a real instance. If you are writing a client for this API, these will bite you:

The component registry also has 13 types, not the four the documentation suggests: Task, GenerativeTask, Model, GenerativeModel, DataLoader, DatasetSource, Metric, Optimizer, Job, LocalExplainer, GlobalExplainer, Explorer, Converter.

And GET /run/{id} returns split_indexes with the full list of indices: on a 10,000-row dataset that is 59 KB, 99% of the response. This server replaces it with the per-split counts, bringing the response down to ~1 KB.

Three design decisions

1. Nine tools, not 142

dashAI exposes 142 REST endpoints. Generating one tool per endpoint is mechanical and it is a mistake: a model with 140 tools burns context reading the catalogue and chooses worse. These nine cover the actual working path.

2. dashai_train_model collapses three calls

In the raw API, training is a chained sequence:

POST /model-session/   → creates the experiment
POST /run/             → creates the run
POST /job/             → enqueues the ModelJob

With required fields the GUI fills in on its own and that are undocumented — plot_history_path, plot_slice_path, plot_contour_path, plot_importance_path. On top of that, splits travels as a JSON string, not an object, even though dashAI's documentation shows it as an object: the backend's Pydantic schema declares it str. That kind of detail is exactly what makes an agent fail against the raw API.

Here it is a single call, and it does not block: training can take hours, so it returns the job_id immediately and progress is polled with dashai_job_status.

dashai_predict does the same for the two-step GUI path: POST /predict/ then POST /job/ with prediction_id.

3. No tool deletes anything

dashAI's API has no authentication — checked endpoint by endpoint. That is coherent for something local-first, but it means there is no barrier between a misread sentence and an irreversible DELETE /dataset/{id}. Deleting is done from the GUI, looking at what is being deleted.

For the same reason, the server refuses to point at a non-local host:

DASHAI_BASE_URL points to 'ml.example.com', which is not local, and dashAI's API
has no authentication: exposing it to the network leaves the backend open to
anyone who can reach it.

This can be disabled on purpose with DASHAI_ALLOW_REMOTE=1, if the target is protected some other way.

Configuration

Development

python -m venv .venv && .venv/bin/pip install -e ".[dev]"
.venv/bin/python -m pytest tests/ -q

The tests stub the HTTP responses with respx: they need neither a dashAI instance nor credentials. They test the contract — which calls are made, in what order, with what body, and what the agent is told when something fails.

A note on the SDK

Requires the MCP Python SDK 2.x. Version 2.0 removed mcp.server.fastmcp; it is now mcp.server.mcpserver.MCPServer, and annotations are ToolAnnotations objects instead of dictionaries. Most tutorials still show the 1.x API.

License

MIT, same as dashAI. See the note at the top on affiliation.