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Ask Other Model

ask_other_model

Ask a rival LLM for a second opinion by sending your question to a specific provider, with optional context to improve the response.

Instructions

Ask a single rival LLM for a second opinion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesModel name, e.g. "gpt-5", "gemini-2.5-pro", "claude-opus-4-7", "grok-4". Provider is inferred from the prefix.
contextNoOptional code snippets, prior reasoning, or files to include.
questionYesThe question to ask the rival model.
system_promptNoOptional persona/framing for the rival.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.2/5.0
Behavior2/5

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

Annotations are absent, so the description must disclose behavioral traits. It only mentions 'ask' and 'second opinion' with no detail on side effects, authentication, response format, or any limitations. This is insufficient for an agent to predict the tool's behavior.

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 sentence that front-loads the core action and scope. It is extremely concise with zero redundancy, achieving maximum efficiency for its content.

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

Completeness2/5

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

For a tool with 4 parameters and no annotations, the description is too sparse. It lacks usage guidance, behavioral context, and any indication of what the output looks like (even though an output schema exists, the description doesn't reference it). An agent is left without enough information to confidently invoke it.

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% with clear descriptions for all parameters (model, context, question, system_prompt). The tool description adds no extra meaning beyond the schema, 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?

States a specific verb (Ask) and resource (single rival LLM) with the purpose of obtaining a second opinion. The word 'single' explicitly distinguishes it from the sibling tool 'ask_the_panel', making the intent unmistakable.

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 on when to use this tool versus the sibling 'ask_the_panel'. The description implies a single opinion but does not state conditions for choosing this over the panel, leaving the agent to infer from the tool name alone.

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

Deploy Server

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