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 has a clearly distinct purpose: train, single-model predict (LR vs KNN), combined predict, metrics, model selection, SHAP explanation, sample input, and audit export. The only mild overlap is predict_both vs the two single-model predict tools, but predict_both is explicitly a combined convenience wrapper and is easy to tell apart.
Nearly all names are consistent snake_case, with most following a verb_noun pattern (train_models, predict_knn, select_best_model, export_parquet_audit). Minor deviations exist: confusion_matrix_metrics is noun-only and shap_explain_selected uses a domain-prefixed verb form.
Nine tools map cleanly onto the ML pipeline stages (train, predict, evaluate, select, explain, export) without excessive granularity. Each tool earns its place with no redundant filler.
The surface covers the full lifecycle: training, individual and combined prediction, evaluation metrics, model selection, explainability, plus audit export and a sample-input helper. Minor gaps remain—no batch prediction or data preprocessing/loading step—but core workflows are fully served.