dashAI (unofficial)

Install

Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/maarmapa-dashai-mcp

About

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this server can do and what it asks for, from its tool list and install config. Checked Oct 25, 2026.

Tools

8 read · 0 write · 0 delete · 2 not yet classified

Secrets

None asked for

Install scripts

Not scanned yet

Runs as

Python package

Tools (10)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• dashai_describe_datasetDescribe a dataset

  Returns everything needed to configure a training run over a dataset. Gathers into a single call what the raw API splits into four (`/{id}`, `/info`, `/types` and `/sample`), because deciding which columns are input and which is output requires seeing them together. Args: params (DescribeDataset): contains: - dataset_id (int): dataset id - include_sample (bool): include sample rows (default True) Returns: str: JSON {"dataset": {...}, "info": {...}, "column_types": {...}, "sample": [...]} If one part is unavailable it comes back as null instead of failing whole.

• dashai_get_predictionPrediction class counts

  Returns class counts for a finished prediction — never the rows. dashAI stores predictions as an Arrow dataset on disk and GET /predict/ only returns the SQL row (id, status, paths). This tool reads that row and, when the job is finished, aggregates the output column. The label list never leaves the function. Args: params (GetPrediction): contains prediction_id from dashai_predict. Returns: str: JSON {prediction_id, run_id, dataset_id, status, finished, n, n_classes, class_counts}. Paths and row lists are stripped.

• dashai_get_runResults of a run

  Returns the configuration and metrics of a training run. This is where results are read once dashai_job_status says `finished`. If the run did not finish, the metrics will come back empty — that is not an error. Args: params (GetRun): contains: - run_id (int): run id Returns: str: JSON with the full run: model parameters, status and metrics per split (train / validation / test).

• dashai_job_statusJob status

  Polls the status of an enqueued job (training, prediction, explanation). dashAI's statuses: `not_started` (queued), `started` (running), `finished` (done) and `error` (failed). Telling `started` from `error` matters: the first is worth waiting on, the second does not improve by polling again. Args: params (JobStatus): contains: - job_id (str): id returned when enqueuing Returns: str: JSON {"job_id": str, "status": str, "finished": bool, "failed": bool, "raw": {...}}

• dashai_list_componentsList available models, metrics and tasks

  Lists the registered components: models, metrics, tasks and optimizers. ALWAYS use this before dashai_train_model. The names dashAI expects are exact and case-sensitive, and the catalogue changes with the plugins that instance has installed — they cannot be guessed. Args: params (ListComponents): contains: - types (Optional[List[str]]): filter by 'Model', 'Metric', 'Task', 'Optimizer' Returns: str: JSON {"count": int, "components": [{"name": str, "type": str, "schema": {...}}]} The `schema` field describes the hyperparameters that component accepts.

• dashai_list_datasetsList datasets

  Lists the datasets loaded in dashAI. Returns only id, name, date and status — just enough to pick one. To see columns and types use dashai_describe_dataset with the id. Args: params (ListDatasets): contains: - limit (int): maximum to return, 1-200 (default 50) Returns: str: JSON {"count": int, "datasets": [{"id", "name", "created", "status"}]} If there are none: a message explaining how to load data from the GUI.

• dashai_list_runsList training runs

  Lists the recorded training runs, with their status. Useful for comparing models trained within the same experiment. Args: params (ListRuns): contains: - model_session_id (Optional[int]): filter by experiment - limit (int): maximum to return, 1-200 (default 50) Returns: str: JSON {"count": int, "runs": [{"id", "name", "model_name", "status", "goal_metric"}]}

• dashai_predictPredict with a trained model

  Enqueues a prediction using the model of an already finished run. Like training, it is asynchronous: it returns a job_id and the result is followed with dashai_job_status. Collapses the two calls the GUI makes (read from DatasetPredictionPanel + createPrediction + enqueuePredictionJob, not from the docs): 1. POST /predict/ creates the Prediction row ({run_id, dataset_id}) 2. POST /job/ PredictJob enqueues with {prediction_id} — NOT run_id Passing only run_id to the job raises KeyError 'prediction_id' inside PredictJob.run. The Prediction row must exist first. Args: par…

• dashai_server_infodashAI server status

  Checks that dashAI is running and summarizes what is loaded. Call this FIRST when something fails or when you do not know whether the backend is up: it tells "dashAI is down" apart from "that id does not exist", which are two problems with different fixes. Args: params (NoArgs): no parameters. Returns: str: JSON with the following schema: { "base_url": str, # which instance is being targeted "reachable": bool, # whether it responded "datasets": int, # number of loaded datasets "runs": int, # number of recorded runs "q…

• dashai_train_modelTrain a model

  Trains a model on a dataset and returns the id of the enqueued job. It does NOT wait for it to finish. Training can take minutes or hours, so dashAI enqueues it and this tool returns immediately; progress is polled with dashai_job_status. Collapses the three calls the raw API demands: 1. POST /model-session/ creates the experiment (dataset, task, columns, metrics) 2. POST /run/ creates the run (model, hyperparameters) 3. POST /job/ enqueues the ModelJob Args: params (TrainModel): contains: - dataset_id (int), task_name (str), model_name (str) …

Signals

Stars

0

Forks

0

Open issues

0

Weekly downloads

48

Last commit

Sep 15, 2026 (1 month ago)

Last release

Sep 15, 2026 (1 month ago)

Last package publish

Aug 25, 2026 (2 months ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name

io.github.Maarmapa/dashai-mcp

Version

0.3.1

Auth

No auth

Packages

pypi dashai-mcp

License

MIT

Repository

github.com/Maarmapa/dashai-mcp

First seen

Oct 24, 2026

Last checked

Oct 24, 2026

Source

Official MCP Registrygithub-graphql