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

Annotations already declare this a safe, idempotent, non-destructive read. Beyond that, the description adds genuine behavioral context: the response shape differs for site-hosted (micro) quizzes, exposes a quiz_type marker and is_connected flag, and explains that is_connected = false means the quiz is not yet attached to any site and has produced no impressions. That last point meaningfully prevents misreading a zeroed funnel.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose before branching into format-specific detail, and every sentence carries information. It is dense but not padded, though the micro-format conditional is packed into one long clause that could be split for readability.

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

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

There is no output schema, so the description must carry return-value meaning — and it does, naming the main metric groups and the alternate funnel fields (impressions, opens, starts, completions, refusals). An agent knows what to expect back, which is the main risk for a no-output-schema analytics tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With a single parameter and 100% schema description coverage, the schema already fully documents quiz_id. The description adds no syntax, format, or constraint information for the parameter, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a concrete verb and resource — it returns summary analytics (visits, devices, geography) for a quiz — and enumerates the actual metric families returned. It is clear enough to distinguish from generic obfuscated read tools, though it never names the closest siblings (get_quiz_report, export_summary_pdf, share_summary_link), so differentiation from those is left to inference.

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

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

There is no explicit statement of when to choose this over get_quiz_report, get_ai_report, or export_summary_pdf, and no prerequisites are given. The conditionals present (micro format changes the response shape) describe output variance, not usage selection, so the agent gets no routing guidance.

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