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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false), the description discloses extensive behavioral details: it fans out to multiple sources in parallel, falls back from GDELT to GNews on rate limits or 5xx errors, notes USPTO soft-fails until reactivated due to API sunset, and specifies the return format (grouped changes with total count and citation URIs). This goes well beyond what annotations 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 well-structured and front-loaded with example queries to quickly convey intent. It is dense but every sentence adds value, covering scope, sources, parameters, fallback behavior, and return format. While comprehensive, it could be slightly more concise by using bullet points for the sources; however, it remains efficient and clear.

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 the tool has three parameters, no output schema, and moderate complexity (multiple data sources, fallback logic, soft-fail condition), the description is remarkably complete. It covers all inputs, explains the internal fan-out behavior, return structure, and edge cases like rate limiting and API sunset. No critical information is missing for correct invocation.

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 input schema already describes each parameter with 100% coverage, the description adds meaningful context: it explains that 'since' accepts ISO dates or relative shorthand like '7d', '30d', '1y', and provides typical values. It also clarifies that 'value' can be a ticker or CIK. This additional usage guidance improves semantics beyond the schema's basic 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 states the tool's purpose: providing a change feed for a company in the last N days/weeks/months via a single parallel call. It lists the specific data sources (SEC EDGAR, GDELT, GNews, USPTO) and distinguishes itself from the sibling tool 'entity_profile' by specifying when to use the latter for static profiles regardless of time window.

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 explicit usage guidance, including example queries like 'What's new with X' and 'latest on Y'. It clearly states when not to use this tool: 'Use entity_profile instead when you want the static profile... regardless of window.' This helps the agent select the correct tool based on context.

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