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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it delivers key transparency by stating 'These are counts from real files rather than an estimate.' It also clarifies the data source (146 real vendor questionnaires) and implies a read-only stats operation. It does not detail edge-case behavior (e.g., unknown questionnaire names), but the provided context is solid.

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 efficiently written: the first sentence packs the resource and output kinds, the second gives direct usage guidance, and the third adds provenance. Every sentence earns its place, and the most decision-relevant information is front-loaded.

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?

For a simple tool with one optional parameter and no output schema, the description adequately explains what the tool returns (topic counts and named questionnaire profiles) and when to use it. It could be more complete about the exact output structure or behavior with unknown named questionnaires, but the agent has enough to select and invoke the tool correctly.

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?

The schema already documents the single optional parameter at 100% coverage, including examples and the omit-for-corpus-wide behavior, so the baseline of 3 applies. The description reinforces this by mentioning HECVAT and CAIQ and the distinction between topic counts and named profiles, but adds no new syntax or format details beyond the schema.

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 uses a specific subject ('security questionnaires') and measurable outputs ('which topics are asked most' and 'detailed profiles'), clearly distinguishing what this tool provides. The mention of HECVAT and CAIQ and the explicit use-case sentence ('Use this when asked what a security questionnaire contains, how long one is, or what a named questionnaire asks about') make the purpose unmistakable and differentiate it from the sibling tools.

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?

The description gives clear context for when to invoke this tool ('Use this when asked...') and explains how to retrieve either corpus-wide counts or a named profile. However, it does not explicitly state when not to use it or compare it to alternatives like search_questions or get_question, so it falls short of a full 5.

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