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

The description goes far beyond the annotations by disclosing the fan-out to SEC EDGAR, the GDELT→GNews fallback on rate limits or 5xx, the USPTO soft-fail due to API sunset, and the return structure (changes[], total_changes, citation URIs). These behavioral traits are not in the annotations and add substantial context for the agent. No contradiction with the readOnly/idempotent hints.

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 serves a purpose: it opens with query examples, explains the multi-source fan-out, includes fallback logic and a sunset warning, defines the `since` parameter, and gives the output shape. No filler or redundancy; well-structured for a complex tool.

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 no output schema, the description gives a solid overview of returns (changes[] grouped by source, total_changes, citation URIs) and covers failure modes (rate limits, API sunset). However, it does not detail the fields within an individual change object, which would be useful for the agent to know what data to expect or extract.

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?

While the schema already covers all three parameters (100% coverage), the description adds semantic depth: it specifies that `since` accepts ISO dates or relative shorthand with examples ('7d', '3m'), and recommends '30d' or '1m' for typical monitoring. It also reinforces that `value` can be a ticker or zero-padded CIK. This extra guidance elevates the score above the baseline 3.

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 defines the tool as a change feed for a company over a time window, with concrete query examples ('What's new with X', 'updates on Acme') and an explicit distinction from entity_profile, which is for static profiles. It clearly states the resource (company changes) and the primary action (return changes from multiple sources).

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 explicitly says when to use this tool ('change feed for a company in the last N days/weeks/months') and provides a clear alternative: 'Use entity_profile instead when you want the static profile...' This gives both positive and negative usage guidance, distinguishing from a sibling tool.

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