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: training, per-model prediction, combined prediction, metrics, model selection, SHAP explanation, sample input, and audit export. The only mild overlap is predict_both versus predict_logistic_regression/predict_knn, but the descriptions make the distinction clear (single model vs. both models with agreement check).
Most names follow a clear verb_noun pattern (train_models, get_sample_input, select_best_model, export_parquet_audit, predict_logistic_regression). A couple deviate into noun-first or nested phrasing (confusion_matrix_metrics, shap_explain_selected), which is a minor inconsistency but still readable and predictable.
Nine tools is well-scoped for a fixed ML workflow covering train, evaluate, select, explain, and predict. Every tool corresponds to a concrete step; none appear redundant or gratuitous.
The surface covers the full lifecycle: training and artifact export, sample input retrieval, single and combined prediction, evaluation metrics, model selection, SHAP explanation, and audit export. Minor gaps exist, such as no tool to list or inspect saved models/artifacts, but core workflows are fully supported.