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

The description discloses significant behavior beyond the readOnly/openWorld/idempotent annotations: fan-out to SEC EDGAR, GDELT/GNews fallback logic, USPTO soft-fail due to PatentsView API sunset, and the exact return shape (changes[] grouped by source, total_changes, citation URIs). This contextualizes what happens during execution without contradicting the 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?

Despite its length, the description is tightly packed with indispensable information. It front-loads the user intent, then explains the multi-source fan-out, parameter format, return structure, and closes with an explicit alternative tool. No sentence is wasted; the density is justified by the tool's complexity.

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?

With no output schema, the description compensates by detailing the return value structure (changes[], total_changes, citation URIs). It also covers edge cases (API sunset, fallback conditions) and the only supported entity type. Combined with the rich annotations and full parameter schema, the description provides a complete operational picture.

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 description coverage is 100%, so baseline is 3. The description adds value by providing concrete examples for 'since' ('7d', '30d', '3m', '1y') and noting typical monitoring values ('Use "30d" or "1m"'), which the schema does not include. It also reinforces the 'type' enum and 'value' format, making the parameter guidance more actionable.

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 defines the tool as a 'change feed' for a company over a time window, with concrete query examples like 'What's new with X' and 'updates on Acme'. It explicitly distinguishes from the sibling tool entity_profile by stating when to use that instead, making the purpose unambiguous.

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?

It provides explicit usage context with natural language examples and a direct alternative: 'Use entity_profile instead when you want the static profile... regardless of window.' It also describes when fallback sources are used (e.g., GDELT preferred, GNews on rate-limit/5xx), which helps an agent decide when to invoke this tool.

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