MCP Machine Learning Agent

predict_both

Predict outcomes using both Logistic Regression and KNN models in one call, then report whether their predictions agree for the supplied input data.

Instructions

Predict with BOTH models in one call and report whether they agree.

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_both"
  }
}

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, and it does disclose the key behavioral trait: both models are executed and their agreement is reported, so the agent knows to expect a comparison rather than a single prediction. It says nothing about failure modes (e.g., what happens when the models disagree), cost of running two models, or whether the call is side-effect free, beyond the audit-log hint in the schema.

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; the differentiator (both models) and the output behavior (agreement) come first. Nothing could be removed without losing information.

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?

The output schema covers the return shape and the input schema fully documents parameters, so the remaining gap is small but real: the description never names which two models are used, which matters for interpreting an agreement result. For a two-parameter inference tool this is adequate but thin.

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 input_data's schema text already explains the 30-feature dictionary and points to get_sample_input, so the description adds nothing about parameters. Baseline 3 is appropriate since the schema does all the work.

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?

States a specific verb (predict) and resource (both models) and adds the distinguishing output behavior (agreement report). It implicitly contrasts with the single-model siblings predict_logistic_regression and predict_knn, though 'BOTH' is never resolved to actual model names in the description.

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: the phrase 'in one call' hints you should pick this over making two separate single-model calls, but there is no explicit when-to-use/when-not guidance and no mention of the sibling alternatives by name.

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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