MCP Machine Learning Agent
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| train_modelsB | Train Logistic Regression and KNN on the breast cancer dataset and export joblib artifacts. |
| get_sample_inputA | Return one test-set patient as a ready-to-use |
| predict_logistic_regressionB | Predict malignant (0) or benign (1) using the saved Logistic Regression model. |
| predict_knnA | Predict malignant (0) or benign (1) using the saved KNN model. |
| confusion_matrix_metricsA | Compute the confusion matrix plus accuracy/precision/recall for both models on the test set. Precision and recall are for the malignant class. Confusion matrix order is [malignant, benign]; rows = actual, columns = predicted. |
| select_best_modelB | Select the best model using a rule (default: recall, then precision, then accuracy). |
| shap_explain_selectedB | Run SHAP for the selected model: global top features plus a local explanation for one test row. Positive SHAP values push toward malignant. |
| export_parquet_auditB | Export a copy of the Parquet audit log for submission. |
| predict_bothA | Predict with BOTH models in one call and report whether they agree. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 9 tools
Each tool maps to a distinct pipeline stage (train, predict, evaluate, select, explain, export), and predict_both is clearly described as a convenience wrapper over the two single-model predict tools. The only mild overlap is predict_both versus predict_logistic_regression/predict_knn, but the descriptions make the distinction explicit.
Most names follow a readable snake_case verb_noun pattern (train_models, get_sample_input, predict_knn, select_best_model, export_parquet_audit). A couple deviate in ordering or form (shap_explain_selected is noun-first, confusion_matrix_metrics is noun-only), but the set remains predictable.
Nine tools is well-scoped for an end-to-end ML demo pipeline, and each one covers a distinct stage with no filler. Nothing feels redundant or missing at the count level.
The surface covers the full lifecycle: train, sample input, per-model prediction, combined prediction, evaluation metrics, model selection, SHAP explanation, and audit export. Minor gaps exist (no tool to inspect dataset/artifact status or list available models before predicting), but agents can work around these via train_models and get_sample_input.