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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses the fan-out architecture to SEC EDGAR, GDELT→GNews fallback, and the PatentsView API sunset condition. It also explains the output shape (changes[] grouped by source, total_changes, citation URIs), which is valuable behavioral context.

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 carries meaningful information: use cases, source routing, parameter format, output structure, and an alternative tool reference. It is front-loaded with the most important intent signals and avoids 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 there is no output schema, the description adequately covers the return format (changes[] grouped by source, total_changes count, citation URIs). It also covers the entity type limitation, `since` parameter semantics, source failure behavior, and when to choose a sibling tool. This is complete for an agent to select and invoke 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% and all three parameters have descriptions, so the baseline is 3. The description adds extra value by giving concrete usage recommendations (e.g., 'Use "30d" or "1m" for typical monitoring') and clarifying that `value` accepts either ticker or zero-padded CIK, which complements the schema.

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 defines the tool as a change feed for a company over a recent window, listing concrete sources and output format. It distinguishes itself from entity_profile by noting that the profile tool should be used for static profiles. The trigger phrases at the start 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?

It explicitly states when to use the tool (recent-change queries like 'What's new' and 'updates on Acme') and provides a direct alternative ('Use entity_profile instead when you want the static profile'). The fallback behavior between news sources and the soft-failure of USPTO also helps set expectations.

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