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

Annotations already declare read-only, idempotent, and open-world hints. The description adds valuable behavioral context: it fans out to SEC EDGAR, GDELT/GNews with fallback rules, and USPTO with a soft-fail due to API sunset. It also explains the return structure (changes[], total_changes, citation URIs) beyond what annotations provide. No contradiction with 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 longer but front-loaded with example queries and core purpose. It packs in sources, fallbacks, parameter formats, return details, and an alternative tool—all relevant. Each sentence earns its place, though a slight trim could improve focus.

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?

Despite having no output schema, the description fully explains return values (changes[] grouped by source, total_changes, citation URIs). It covers external API dependencies, failure modes, parameter options, and when to use a sibling tool, making it complete for an agent to 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?

While schema coverage is 100%, the description enriches parameter meaning with concrete examples: `since` accepts ISO dates or relative shorthand ('7d', '30d', '3m', '1y') and recommends '30d' or '1m' for typical monitoring; `value` is exemplified with 'AAPL' and CIK '0000320193'. This goes beyond mere schema descriptions.

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 in the last N days/weeks/months' and provides concrete example queries like 'What's new with X' and 'updates on Acme'. It also explicitly differentiates from the sibling entity_profile, which retrieves static profiles, making the purpose unmistakable.

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 use cases through example queries and states when to use an alternative: 'Use entity_profile instead when you want the static profile...' This provides clear when-to-use and when-not-to-use guidance, plus the fallback behavior for news sources.

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