GitHub Models Helper

compare_models

Compare AI model outputs for a single prompt. Send the same prompt to multiple models and review their responses side-by-side to choose the best fit.

Instructions

Compare responses from different models for the same prompt

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": {
      "prompt": "<prompt>"
    },
    "name": "compare_models"
  }
}

Input Schema

Name	Required	Description	Default
`models`	No	List of model IDs to compare
`prompt`	Yes	The prompt to send to all models

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 10, 2026

TDQS

B3.1/5.0

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 burden of behavioral disclosure. It only states the action without telling whether the call is synchronous, how results are presented, whether it has side effects (e.g., costs, rate limits), or what happens if a model fails. This is a significant gap for a tool that compares models.

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?

The description is a single, direct sentence that starts with the verb 'Compare'. It is concise, front-loaded, and contains no filler words. Every word earns its place.

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 tool has modest complexity (2 params, no output schema). The description states the core purpose, but without annotations or an output schema it lacks detail on return values and behavioral context. It is a bare minimum viable description, but not rich enough for an agent to fully anticipate the result.

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 coverage is 100%, so the schema already documents both 'prompt' and 'models'. The description adds a minimal semantic hint ('same prompt') but doesn't explain nuances like whether 'models' is an ordered list or how defaults behave. Baseline 3 is appropriate as 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 clearly states the action ('Compare') and the resource ('responses from different models') along with the context ('same prompt'). However, it doesn't specify what 'compare' means (e.g., side-by-side output, diff, metrics) and there are no sibling tools to differentiate from, so it falls short of a 5.

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?

No guidance is provided on when to use this tool, preconditions, or alternatives. The description is purely functional and doesn't mention any exclusions or scenarios where another tool would be preferred. Since the schema shows it requires a prompt, but that's structural not usage guidance.

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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