MCP Machine Learning Agent

train_models

Train Logistic Regression and KNN classifiers on the breast cancer dataset, then export both trained models as joblib artifacts for later prediction and evaluation.

Instructions

Train Logistic Regression and KNN on the breast cancer dataset and export joblib artifacts.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "train_models"
  }
}

Input Schema

Name	Required	Description	Default
`test_size`	No	Fraction of the data held out for testing.
`request_id`	No	Your own id for this call; recorded in the audit log.	unknown
`random_state`	No	Random seed for the train/test split.
`knn_neighbors`	No	Number of neighbors for the KNN model.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Nov 9, 2026

TDQS

B3.3/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It does disclose one useful trait — that the call exports joblib artifacts, implying a filesystem write — but says nothing about whether existing artifacts are overwritten, where they are written, expected runtime, or any permissions/auth needed for a mutation-style tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler: verb, models, dataset, and output all appear before any padding. Nothing in the sentence is redundant with the schema or title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present and full parameter documentation, the return values and arguments are covered. What remains missing for a training/export tool is behavioral context: overwrite semantics for the artifacts, ordering relative to the predict/select siblings, and any side-effect warnings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the schema documents all four parameters (test_size, random_state, knn_neighbors, request_id) with defaults and bounds. The description adds no parameter-level meaning beyond that, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb (Train), two concrete model types (Logistic Regression and KNN), the exact dataset (breast cancer), and the side effect (export joblib artifacts). This is unambiguous and clearly separable from the sibling prediction tools (predict_logistic_regression, predict_knn) and from select_best_model.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to call this versus its siblings, e.g. whether it must be run before predict_logistic_regression/predict_knn, whether it can be re-run, or how it relates to select_best_model. The usage context is only implied by the verb 'Train'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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