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
Most tools target clearly distinct lifecycle steps: training, sampling input, per-model prediction, combined prediction, evaluation, selection, explainability, and audit export. The only mild overlap is between predict_knn/predict_logistic_regression and predict_both, but the descriptions make the distinction (single model vs. both-plus-agreement) clear enough.
The set is uniformly snake_case and largely verb_noun (predict_knn, train_models, select_best_model, export_parquet_audit). However, confusion_matrix_metrics has no verb and shap_explain_selected uses an inverted/noun-first ordering, breaking the otherwise predictable pattern.
Nine tools is well-scoped for an ML agent covering train, predict, evaluate, select, explain, and export. Each tool earns its place with no filler or redundant entries.
The surface covers the full workflow: training, sample input retrieval, inference (per-model and combined), metrics, model selection, SHAP explanation, and audit export. Minor gaps exist, such as listing or inspecting saved model artifacts/metadata, but agents can work around these.