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Готовый умный отчёт

get_ai_report
Read-onlyIdempotent

Отдаёт готовый AI-отчёт по опросу — тот самый, что автор видит в разделе результатов. Отчёт состоит из трёх независимых разделов: текстовый анализ, сравнительный и количественный. У каждого свой статус: готов, строится прямо сейчас или не построен, и почему — мало ответов либо нет подходящих вопросов. Читать отчёт этим инструментом, а не запускать генерацию заново: она тратит лимит тарифа.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
quiz_idYesID опроса.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, so the safety profile is covered. The description adds real behavioral context beyond them: the report has three independent sections (textual, comparative, quantitative) each with its own build status and failure reasons (too few responses, no suitable questions). It stops short of describing return format or how partial readiness is surfaced, so not a 5.

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 first sentence states what is returned, the second explains the structure, the third explains statuses, and the last gives the routing instruction. Front-loaded and every sentence carries information.

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?

With no output schema, the description valuably pre-announces the return shape (three sections with per-section statuses and reasons), which is what an agent needs to interpret the response. It still doesn't fully specify the response envelope or how statuses are encoded, leaving a small 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?

Only one parameter (quiz_id) and schema coverage is 100%, so the schema fully documents it. The description adds nothing about the parameter's meaning or format, which is the expected baseline when the schema does the work.

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?

Names a specific verb+resource ('Отдаёт готовый AI-отчёт по опросу') and pins down the scope by saying it is the same report the author sees in the results section. It is immediately distinguishable from generate_ai_report in the sibling list.

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

Usage Guidelines5/5

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

Explicitly instructs to read the report with this tool rather than re-triggering generation, and states the cost reason (generation consumes the tariff limit). This is a clear when-to-use-this-vs-alternative directive, which is exactly the sibling ambiguity an agent faces.

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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