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

Even with readOnlyHint=true and destructiveHint=false annotations, the description adds substantial behavioral detail: it fans out to multiple sources, explains GDELT→GNews fallback conditions, discloses USPTO soft-fail due to API sunset, and describes the 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 every sentence earns its place: it includes usage examples, source specifics, fallback logic, parameter hints, return format, and an alternative tool recommendation. It is front-loaded and well-structured, with no irrelevant filler.

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 multi-source fan-out complexity and absence of an output schema, the description is remarkably complete. It explains all data sources, fallback behavior, parameter accepted formats, return structure, and when to use an alternative tool. No critical information appears missing for an agent to select and invoke it correctly.

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%, with each parameter already described in detail (type: 'company', since: ISO/relative, value: ticker/CIK). The description does not add new parameter-level meaning beyond what the schema provides; it mostly reinforces the `since` semantics and mentions return values, which is not parameter-specific. Baseline 3 is appropriate.

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's function: a change feed for a company in the last N days/weeks/months, covering SEC filings, news mentions, and patents. It uses specific verbs ('Fans out', 'Returns') and explicitly differentiates from the sibling tool entity_profile ('Use entity_profile instead when you want the static profile...').

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 with example queries ('What's new with X' / 'latest on Y') and explicitly names the alternative tool to use for static profiles regardless of window. It also explains fallback behavior (GDELT preferred, GNews on rate-limit/5xx) which informs usage expectations.

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