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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses meaningful behavior: first-call embedding model load latency, deduplicated story clusters rather than raw documents, explicit zero rows, and named empty_aspects. These are exactly the kind of non-obvious behaviors an agent needs to interpret results correctly.

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 dense but every clause adds operational value: scope, return shape, data semantics, accepted values, defaults, related lookup, and a performance warning. It is front-loaded with purpose and alternatives before diving into output details, so an agent scanning quickly gets the decision-critical information first.

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 no output schema, the description fully explains return structure: aspect-by-time grid, cluster counts, variance, polarity histogram, language/source breakdowns, and source URLs. It also covers missing-data conventions and startup latency. This is unusually complete for a read-only aggregate query 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?

Schema description coverage is 0%, so the description must carry parameter meaning. It explains geo_id, window_start/window_end ISO-8601 defaults, bucket granularity, aspects subset, and top_k via 'top-k canonical source URLs'. The only clear gap is 'langs', whose filtering semantics are left to inference from the field name and return breakdown.

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 resource (destination sentiment aggregates for Product A) and a clear use case ('how do travelers feel about X over time'). It explicitly distinguishes itself from real-time alert/standing-query tools, so an agent can tell it apart from siblings.

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 direct when-to-use guidance ('use for ... over time') and an explicit negative ('never for real-time alerts — that is the standing-query/event side'). It also directs the agent to resolve_geo for finding geo_ids, which is actionable.

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