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

Annotations already declare readOnlyHint=true and idempotentHint=true, so safety is clear. The description adds essential behavioral context: it fans out to multiple sources in a single parallel call, explains the GDELT→GNews fallback, notes the USPTO PatentsView API sunset, and specifies the return shape (changes[] grouped by source, total_changes, citation URIs). This goes far beyond the structured 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-organized, front-loading with user intent phrases before diving into technical specifics. Every sentence contributes: purpose, source breakdown, fallback behavior, parameter format, return structure, and sibling differentiation. No filler or redundancy.

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 complexity (multiple upstream APIs, fallback chains, soft-fail conditions) and absence of an output schema, the description is remarkably complete. It explains return structure, source grouping, citation URIs, and the `since` format. Annotations cover idempotency and read-only safety, so the agent has enough context to 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%, so the baseline is 3. The description enhances parameter understanding by giving concrete examples (ISO date vs '7d' shorthand) and recommending '30d' or '1m' for typical monitoring. The value parameter is also clarified with ticker or zero-padded CIK examples, which adds practical guidance over the schema alone.

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 concrete user-intent examples ("What's new with X"). It explicitly distinguishes itself from the sibling entity_profile, which returns a static profile. The verb+resource+scope is specific: "change feed for a company in the last N days/weeks/months."

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 explicit usage context (when to query recent changes) and directs users to entity_profile when they want the static profile regardless of window. It also discloses fallback behavior (GDELT→GNews, USPTO soft-fail) so the agent knows 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.