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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds substantial value beyond that by disclosing the return shape: matched use case(s), up to 8 standards ranked by fit/maturity/adoption with reasons, do_this_first prerequisites, deprecated standards to avoid, and also_consider contents.

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?

Purpose and examples are front-loaded in the first clause, and the remainder packs input and output detail without redundancy. It reads as a dense single block rather than cleanly separated sentences, which is slightly harder to scan but still earns its length.

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 explain returns, and it does so thoroughly (ranked standards, reasons, prerequisites, deprecated items, related items). Inputs are covered at a high level; only the finer per-parameter constraints are left to the schema.

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?

Schema description coverage is 0%, so the description must carry the param burden. It does characterize question as free-form plain words with examples and flags use_case as one of 15 schema ids plus audience, but it adds no constraint detail (e.g. the 200-char limit) and defers the enum values to the schema, so compensation is only partial.

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 specific verb+resource: ask a plain-language question about what to implement for a goal, with concrete examples ('let agents pay per request'). It is clearly a recommendation/guidance tool distinct from data-oriented siblings like get/search/changes, but it never explicitly names or contrasts a sibling, so it stops short of a 5.

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?

Usage is implied through example questions ('stop AI training crawls', 'let ChatGPT call my API') and the note that use_case/audience are optional, which conveys how to invoke it. However, there is no explicit when-to-use vs when-not-to-use guidance against siblings such as search, define, or template, leaving routing to inference.

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