Check how this MCP server can currently reach Altair AI Studio 2026.0.5: whether the optional live HTTP bridge extension is running, and whether the headless batch script (rapidminer-batch.bat) is reachable at ALTAIR_HOME. Run this first when troubleshooting.

List folders/entries in the Altair AI Studio repository (requires the HTTP bridge extension, since the repository lives inside the running Studio session). Example path: "//Local Repository" or "//Samples/data".

Read a data entry from the Altair AI Studio repository as CSV (requires the HTTP bridge extension).

Write a local CSV file's contents into an Altair AI Studio repository entry (requires the HTTP bridge extension). Covers action 82-83 style export-to-repository / reuse-as-input flows.

Import/preview tabular data through Altair AI Studio, from a repository entry or a local CSV file. Covers CSV/repository ingestion (actions 1-2, 5-7, 9 of the data-ingestion category).

Clean a dataset: replace missing values, remove duplicate rows, and optionally drop named columns. Covers missing-value handling, duplicate removal, and column selection (actions 11-15, 20 of data preparation).

Normalize/scale numeric attributes (Z-transformation, range/min-max, proportion, or interquartile range). Covers actions 17-18 (normalize and scale variables).

Create a new derived/calculated column using a RapidMiner expression, e.g. attributeName="profit", expression="revenue - cost". Covers action 21 (create new variables via formulas).

Compute descriptive statistics (average, min, max, standard deviation, median, count) for every numeric column. Covers actions 26-28 (descriptive statistics, distribution summary).

Split a dataset into train/test partitions. Covers action 66 (train/validation/test split).

Train and evaluate a classification model with k-fold cross validation (Decision Tree, Random Forest, Naive Bayes, k-NN, SVM, Logistic Regression, Neural Net, or Gradient Boosted Trees). Returns the performance vector (accuracy/precision/recall/etc). Covers actions 46-55, 66-74 (supervised learning + validation).

Segment data with k-Means clustering; returns the dataset with an added cluster-id column. Covers actions 56, 60, 64-65 (segmentation / clustering).

Mine association rules with FP-Growth + Create Association Rules (market-basket analysis). Input data must be in transactional/binominal (item present/absent) form. Covers actions 61-64 (association rules, support/confidence/lift, market basket analysis).

Reduce dimensionality with PCA, keeping enough components to reach a target variance threshold. Covers action 43 (PCA).

ADVANCED / escape hatch: run ANY Altair AI Studio / RapidMiner operator graph you assemble yourself, by giving the exact operator class keys, parameters, and port-level connections. Use this for operators not covered by the dedicated tools (DBSCAN, hierarchical clustering, database connections, web/text/scraping extensions, Hugging Face / LLM operators, Optimize Parameters, Loop Files, Python/R scripting, etc). Tip: build the graph once in the Altair AI Studio GUI and use Process > Export Process to see the exact class keys and port names to copy here. The graph must end by writing its result(s) with a 'write_csv' operator to an absolute file path you choose — read that file back afterwards to see the result.

Execute an existing .rmp process file (already saved on disk or exported from the Studio repository) headlessly via rapidminer-batch. Covers actions 76-80 (run saved/repeatable workflows, automate experiments) and scoring-on-new-data flows (action 84).

Read the XML of the process currently open in the Altair AI Studio GUI (requires the HTTP bridge extension). Useful to see what the human user is working on before suggesting changes.

Replace the process currently open in the Altair AI Studio GUI with the given XML, handing control back to the human user so they can inspect/run/edit it visually (requires the HTTP bridge extension).