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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds significant behavioral context beyond these: it fans out to multiple sources (SEC EDGAR, GDELT→GNews fallback, USPTO), notes the PatentsView API sunset causing soft-fail, and specifies the return format (structured changes[] grouped by source + total_changes + pipeworx:// citation URIs).

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: user-intent examples first, then capability, data-source breakdown, parameter syntax, and return format, ending with an explicit alternative. Every sentence packs useful information, though slightly long due to the fallback and sunset details. Still, nothing is wasted.

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?

For a tool with this complexity (multiple data sources, fallback logic, time-window parameters, and custom return structure), the description covers all necessary aspects: what sources are queried, how failures are handled, what the return includes, and how to disambiguate from entity_profile. No output schema exists, so the description's mention of changes[] and total_changes is essential and adequate.

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% (all three parameters documented). The description enriches this by showing examples for `since` ('2026-04-01' or '7d', '30d', '3m', '1y') and suggests typical values. It also clarifies `value` accepts ticker or zero-padded CIK and that `type` only supports 'company'. This adds value beyond the raw 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 opens with concrete user intents ('What's new with X' / 'latest on Y') and then defines the tool as a 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It clearly distinguishes itself from sibling entity_profile by explicitly pointing to the 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?

Usage guidance is explicit: 'Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.' It also provides contextual recommendations like 'Use "30d" or "1m" for typical monitoring' and explains fallback behavior (GDELT→GNews when rate-limited or 5xx).

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