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SurveyNinja

by io.surveyninja
OverviewDiscussionsAdmin

Ready AI report

get_ai_report
Read-onlyIdempotent

Returns the ready AI report for the quiz — the very one the author sees in the results section. The report has three independent sections: text analysis, comparative and quantitative. Each carries its own status: ready, being built right now, or not built and why — too few answers or no eligible questions. Read the report with this tool rather than regenerating it: generation spends the plan limit.

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": {
      "quiz_id": "<quiz_id>"
    },
    "name": "get_ai_report"
  }
}

Input Schema

NameRequiredDescriptionDefault
quiz_idYesQuiz ID.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

The description goes beyond standard annotations by detailing the internal structure of the report (text, comparative, and quantitative sections) and possible states for those sections (ready, building, not built). This is highly valuable context not provided by the schema or annotations.

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 highly informative but verbose. It doesn't waste words by reiterating the name or schema, but it achieves its goal by front-loading the core function and then providing essential operational context and structure.

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

Completeness5/5

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

Since there is no output schema, the description is maximally complete, providing a detailed description of the report content, structure, and internal statuses, allowing the AI agent to predict the nature of the returned data.

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?

The input schema covers 100% of the parameter definition (quiz_id). While the description names the resource needing the ID, it does not add any semantic meaning or constraint beyond what is already established by the schema.

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 explicitly defines the tool's function as returning a specific type of AI report for a quiz. It distinguishes this specific 'ready AI report' from general report generation, helping the agent select it over generic reporting tools.

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?

It provides crucial guidance on operational cost, advising the agent to use this read operation instead of regenerating the report, which 'spends the plan limit.' This is direct and actionable advice for the agent.

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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