MCP Machine Learning Agent

select_best_model

Chooses the model to use by applying a selected metric rule (default: recall, then precision, then accuracy) to compare trained classifiers and decide which one to use.

Instructions

Select the best model using a rule (default: recall, then precision, then accuracy).

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": "select_best_model"
  }
}

Input Schema

Name	Required	Description	Default
`rule`	No	One of: recall_then_precision_then_accuracy, accuracy, precision, recall. Precision/recall refer to the malignant class.	recall_then_precision_then_accuracy
`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

B3.2/5.0

Behavior2/5

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

With no annotations, the description carries the full behavioral burden. It discloses the default rule order but says nothing about whether this mutates/persists a 'selected model' state, what preconditions exist (trained models), or any side effects. For a selection tool with zero annotation coverage this is a significant gap.

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

Conciseness4/5

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

A single tight sentence that front-loads the action and includes the default. No filler, though it is perhaps too terse to be fully useful given the missing behavioral and usage detail.

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?

An output schema exists, so return values need not be explained. However, for a tool operating on models produced by siblings, the description omits prerequisites and whether the selection persists, leaving key context incomplete.

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%, so all five parameters (rule, test_size, request_id, random_state, knn_neighbors) are already documented in the schema. The description only restates the default rule, adding nothing beyond what the schema provides. Baseline 3 applies when the schema does the heavy lifting.

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

Purpose4/5

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

The description states a specific verb+resource: it selects the best model using a scoring rule. An agent can tell this apart from prediction and training siblings (predict_knn, train_models), though the description doesn't explicitly name what pool of models it selects from. Clear but without mindful sibling differentiation.

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

Usage Guidelines3/5

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

Usage is only implied — after training/comparing models you pick the best one by a rule. There is no statement of prerequisites (e.g., models must already be trained), no when-not-to-use, and no routing to siblings like confusion_matrix_metrics for inspecting metrics. Adequate but with clear gaps.

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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