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

The description discloses multi-source fan-out, fallback behavior (GDELT→GNews), and soft-fail for USPTO. It also describes the return structure (changes[], total_changes, citation URIs), adding significant value beyond the annotations, which already mark it as read-only, idempotent, and non-destructive. No contradictions.

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 moderately sized but every sentence adds value: example queries upfront, then core functionality, parameter explanations, return structure, and sibling differentiation. It is well-structured and front-loaded with the most important information.

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's complexity (multiple sources, fallback, error handling) and lack of output schema, the description provides sufficient context for an AI agent to invoke it correctly. It covers return format, parameter formats, and edge cases like USPTO soft-fail.

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?

With 100% schema coverage, the schema already describes each parameter, but the description adds critical context: `since` details (ISO date vs relative shorthand, recommendations), `type` restriction, and `value` acceptance of ticker or CIK. This enhances the AI agent's understanding beyond the schema alone.

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 over a recent time window, citing specific sources (SEC EDGAR, GDELT/GNews, USPTO). It distinguishes from the sibling tool 'entity_profile' by recommending that tool for static profiles, 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?

The description begins with example queries ('What's new with X', 'latest on Y') that indicate typical use cases. It explicitly states when to use an alternative ('Use entity_profile instead when you want the static profile'), providing clear usage guidance.

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