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 have clearly distinct purposes: training, sample input, individual predictions, metrics, model selection, SHAP explanation, and audit export. Minor overlap exists between predict_both and the individual predict tools, and between confusion_matrix_metrics and select_best_model, but descriptions make the intended use clear.
All names use snake_case consistently, and most follow a verb_noun or verb_object pattern. A few deviate, such as confusion_matrix_metrics (noun phrase) and shap_explain_selected (awkward ordering), but the overall convention is predictable.
Nine tools are well-scoped for a compact ML workflow: training, sample retrieval, two predictions plus a combined prediction, evaluation, selection, explanation, and audit export. Each tool has a clear role without excessive surface area.
The surface covers the full workflow from training and sample input through prediction, evaluation, model selection, explainability, and audit export. There are no obvious dead ends for the stated breast-cancer-dataset ML-agent purpose.