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

Annotations already indicate read-only, idempotent, open-world, and non-destructive behavior. The description adds value by detailing the parallel fan-out to SEC EDGAR, GDELT/GNews fallback logic, USPTO soft-fail due to API sunset, and the exact return structure (changes[], total_changes, citation URIs). This goes well beyond the annotations.

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-structured, starting with user-intent examples, then the core definition, fan-out details, and return format. While slightly long, every sentence contributes meaningful information. The structure aids comprehension despite the length.

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?

The tool is complex with multiple sources, date handling, and fallback logic. The description covers all key aspects: sources, fallback behavior, date formats, return shape, and the alternative tool. Since there is no output schema, explaining the return structure is essential and is done clearly.

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 coverage is 100%, so baseline is 3. The description adds practical guidance for the `since` parameter (ISO date vs relative shorthand, recommended '30d'/'1m') and provides examples for `value` (ticker or CIK). This exceeds schema descriptions, though not exhaustively for all edge cases.

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 a change feed for a company over a time window, with specific verbs like 'fans out' and 'returns'. It distinguishes from the sibling entity_profile by targeting dynamic changes rather than a static profile. Example queries make the purpose immediately recognizable.

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?

Explicitly states when to use this tool ('change feed for a company in the last N days/weeks/months') and when to use an alternative ('Use entity_profile instead when you want the static profile'). Also describes fallback behavior (GDELT→GNews) and soft-fail conditions, giving clear context for when results may be incomplete.

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