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

Annotations already declare this as safe, read-only, idempotent, non-destructive, so the baseline is met. The description adds valuable behavioral context: results are ranked by predicted impact and each event returns rank, predicted attendance, dates, and location. It does not repeat the annotation flags, and nothing contradicts them.

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?

Three sentences and one example, front-loaded with the core purpose and filter capabilities. The example is compact and shows realistic usage, and the description avoids redundancy with the schema. No wasted words.

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?

With 7 parameters, full schema coverage, and no output schema, the description compensates by explicitly stating the return fields (rank, predicted attendance, dates, location). It also orients the agent to the use case (demand forecasting) and gives a complete example. This makes the tool well-specified for selection and invocation.

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 descriptions cover 100% of the 7 parameters with detailed field-level explanations and an example, so the baseline is 3. The description adds meaning by explaining that 'active-date window' uses start/end and that results are ranked by impact, plus a concrete example combining parameters. It doesn't repeat every schema field but provides enough to understand parameter interplay.

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 opens with 'Search the PredictHQ Events API' naming both verb and resource, and specifies the domain (real-world events for demand forecasting). It lists example categories and key filters, making the tool's function unmistakable even without reading sibling names. Though it doesn't explicitly contrast with nearby_events, the scope is clearly distinct.

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: use for demand forecasting with free-text, category, country, and date filters, results ranked by impact. It implies when to use this tool (structured event search) but doesn't explicitly state exclusions or alternatives like nearby_events. The schema notes keyless alternatives (ticketmaster, seatgeek) but the description itself does not provide when-not-to-use guidance.

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