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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: it clarifies that results are anonymous and aggregate, that small groups are suppressed for privacy, and that no individual post is identifiable. This goes beyond annotations and is particularly important for a data aggregation tool.

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 concise and front-loaded: it starts with the core purpose and then adds a privacy note. Every sentence adds value—no filler or redundancy. It's appropriately sized for the complexity of the tool.

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?

Given the tool's complexity (aggregate statistics with grouping, suppression, privacy) and the lack of an output schema, the description is fairly complete: it tells what data is returned and the privacy safeguards. However, it doesn't specify the exact output structure, which an agent might need to interpret results, but since no output schema exists, the description could go further in that regard. The main gap is parameter semantics, but overall it covers the essentials.

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 compensate, and it does partially: it explains what the tool returns (categories, areas, wants vs available) but doesn't explicitly describe how the parameters map to those dimensions. For example, 'region' should correspond to 'area', 'category' to 'category', and 'group_by' to the grouping dimension. The description implies these mappings but doesn't state them clearly, leaving some ambiguity for an agent.

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 clearly states the tool provides anonymous, aggregate market statistics, specifying what it measures (unmet demand, budgets vs asking prices, most-wanted features) and the grouping dimensions. It distinguishes itself from siblings like search or find_matches by focusing on aggregate statistics rather than individual posts, but it doesn't explicitly name a sibling alternative.

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 implies when to use this tool: for market-level insights, not individual post details. It mentions suppression of small groups and no identifiable individual posts, which helps an agent decide against using get_post or search for this purpose. However, it doesn't explicitly state when NOT to use it or mention alternatives.

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