Store API

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ask_model

Ask any model from the Store API catalog — GPT, Claude, Gemini, DeepSeek, Grok, Qwen, GLM — and get the answer as text. Lets you consult a second model from inside the current chat. — Задать вопрос любой модели каталога и получить ответ текстом.

Input Schema

TableJSON Schema

Name	Required	Description
`model`	Yes	Model slug, e.g. openai/gpt-6-sol or anthropic/claude-sonnet-5. Get the list from list_models
`prompt`	Yes	Question or task for the model
`system`	No	System instruction, optional
`max_tokens`	No	Answer length limit, 1024 by default

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No
`model`	No
`answer`	No
`input_tokens`	No
`output_tokens`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 24, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "answer": {
+      "type": "string"
+    },
+    "error": {
+      "type": "string"
+    },
+    "input_tokens": {
+      "type": "number"
+    },
+    "model": {
+      "type": "string"
+    },
+    "output_tokens": {
+      "type": "number"
+    }
+  },
+  "type": "object"
+}

First observedSep 24, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=false and openWorldHint=true, so the agent knows this is not a read-only operation and may access external data. The description adds that it queries a catalog model and returns text, but does not disclose potential costs, latency, or any side effects. With annotations covering the safety profile, a 3 is appropriate—some added context but not rich behavioral detail.

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?

The description is two sentences (plus a Russian translation) and is front-loaded with the primary action. The second sentence adds context about consulting a second model, which is not redundant. The bilingual duplication is minor but arguably unnecessary for an AI agent; overall it is appropriately sized and efficient.

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

Completeness4/5

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

Given the tool's simplicity, 4 parameters fully described in the schema, and the presence of an output schema, the description is complete enough for an agent to call it correctly. It covers the purpose and the primary use case. Nothing critical is missing; the output format is handled by the output schema.

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 four parameters (model, prompt, system, max_tokens) are already documented in the schema. The description does not add any parameter-specific guidance beyond what's there. Per the rubric, a baseline of 3 is correct when the schema handles the documentation.

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?

The description states a specific action ('ask any model') and a clear resource (Store API catalog), and names the model families it covers. It also frames the unique value ('consult a second model from inside the current chat'), which distinguishes it from siblings like list_models or compare_models. No ambiguity about what the tool does.

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

Usage Guidelines4/5

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

The description gives a clear usage context: when you want to consult a second model within the chat. It does not explicitly state when not to use it or point to alternatives (e.g., compare_models for comparison), but the context is evident from the purpose and the sibling list. This is 'clear context, no exclusions', which matches a 4.

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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