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

With annotations already declaring read-only, open-world, idempotent, and non-destructive behavior, the description adds valuable operational context: it fans out to multiple APIs in one parallel call, details fallback behavior, and mentions the PatentsView sunset. This goes beyond the annotation baselines.

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 well-organized, starting with user-intent examples and moving through the data sources, parameters, and return format. It's longer than average but every sentence carries meaning; still, a bit more compression would be possible.

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?

Without an output schema, the description explains the return value structure (changes[] grouped by source, total_changes, citation URIs). It also covers the multi-source fan-out and failure modes, making it fairly complete for a complex tool. Missing granular detail on the shape of each change item, but acceptable.

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 covers all three parameters at 100% detail. The description enriches this by explaining the accepted formats for `since` (ISO or relative shorthand) and recommending "30d" or "1m" for typical monitoring, which adds practical guidance beyond the schema listings.

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 identifies the tool as a change feed for a company over a time window, with explicit examples of natural language queries. It also distinguishes itself from the sibling tool entity_profile by directing users to that alternative for static profiles.

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 provides explicit when-to-use guidance via query examples and even recommends a fallback tool (entity_profile) for static profiles. It also details the internal fallback logic between data sources (GDELT to GNews) and notes the USPTO soft-fail condition, giving clear context for trade-offs.

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