MCP Machine Learning Agent

predict_knn

Predict whether breast cancer is malignant or benign by passing all 30 features to the saved KNN model.

Instructions

Predict malignant (0) or benign (1) using the saved KNN model.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "input_data": "<input_data>"
    },
    "name": "predict_knn"
  }
}

Input Schema

Name	Required	Description	Default
`input_data`	Yes	Feature dictionary with all 30 breast cancer features as numbers, keyed by their exact names (e.g. 'mean radius', 'worst area'). Use get_sample_input to get a valid example.
`request_id`	No	Your own id for this call; recorded in the audit log.	unknown

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Nov 9, 2026

TDQS

A3.5/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses the class-label mapping (0=malignant, 1=benign), which is real behavioral value, but says nothing about the prerequisite trained-model state, failure behavior when no model is saved, or whether the call is a pure read.

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 zero waste. The action, model, and output interpretation are all in the first clause.

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 no explanation, and the schema points to get_sample_input for valid feature examples. However, for a no-annotation inference tool the description omits the key precondition (a previously trained/saved KNN model) and any failure mode, leaving a real gap.

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 both parameters are fully documented in the schema (input_data keyed by exact feature names, request_id for the audit log). The description adds no further parameter meaning, so the baseline 3 applies.

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?

Specific verb (predict), specific resource (the saved KNN model), and even the output label semantics (0=malignant, 1=benign). The model name implicitly separates it from the predict_logistic_regression sibling, though the description never explicitly contrasts them.

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?

There is no explicit when-to-use/when-not statement or routing between the sibling predictors. The phrase 'the saved KNN model' only implies the prerequisite that train_models must have already run, leaving usage to inference.

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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