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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, but the description adds rich behavioral detail: fan-out to multiple sources, GDELT→GNews fallback, USPTO soft-fail, and grouping of results. No contradictions with annotations. This exceeds what annotations alone provide.

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 front-loaded with example queries, making the tool's intent instantly clear. Despite its length, every sentence adds necessary detail about sources, fallbacks, parameter formats, and return structure. No redundant or vague statements.

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 3 parameters, 100% schema coverage, no output schema, and no nested objects, the description is remarkably complete. It explains the tool's multi-source behavior, parameter options, output structure (changes[], total_changes, citation URIs), and provides sibling differentiation. No additional details are needed for an AI agent to invoke the tool 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 high. The description adds value by explaining accepted formats for 'since' (ISO date or relative shorthand like '7d', '30d', '3m', '1y') and providing a concrete monitoring recommendation. It reinforces the 'value' parameter can be ticker or CIK, and the 'type' enum is limited to 'company'. This aids parameter utilization beyond 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 states the tool provides a 'change feed for a company in the last N days/weeks/months' and lists specific data sources (SEC EDGAR, GDELT→GNews, USPTO). Example queries like 'what's new with X' and 'latest on Y' make the purpose immediately recognizable. It explicitly distinguishes from sibling tool 'entity_profile', which provides 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?

The description provides strong guidance on when to use this tool versus entity_profile: 'Use entity_profile instead when you want the static profile.' It also gives example queries and parameter hints (e.g., '30d' for typical monitoring). However, it doesn't explicitly state when not to use it beyond the sibling comparison.

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