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. |
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 8 tools
Each tool targets a distinct stage of the ML pipeline (train, sample input, per-model predict, metrics, selection, SHAP, export). The only mild overlap is predict_logistic_regression vs predict_knn, but the names and descriptions clearly distinguish which model each uses.
Most tools follow a verb_noun pattern (train_models, predict_knn, select_best_model, export_parquet_audit). A couple deviate into noun-heavy forms (confusion_matrix_metrics, shap_explain_selected), but the set remains readable and predictable.
8 tools is well-scoped for a focused ML agent, mapping cleanly onto the train/evaluate/select/explain/export lifecycle. No filler tools and no obvious missing step.
The pipeline covers training, per-model prediction, evaluation, model selection, explainability, and audit export. Minor gaps exist (e.g. no predict endpoint for arbitrary new input beyond the provided sample, no listing of saved artifacts), but these are workable around.