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

The description discloses the fan-out logic, fallback behavior (GDELT preferred, GNews when rate-limited or 5xx), and USPTO soft-fail status. It also outlines the return structure (grouped changes, total count, citation URIs). This adds valuable context beyond the annotations, which already indicate idempotency and read-only nature.

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 a single dense paragraph but efficiently packs all necessary information. It front-loads the purpose with user intents. Slightly more structure (e.g., bullet points) could improve readability, but it remains concise without fluff.

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 complexity of fan-out across multiple APIs with fallback logic and parameter flexibility, the description covers all critical aspects. Even without an output schema, it describes the return format, making the tool fully usable.

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%, yet the description enhances each parameter: `since` explains both ISO and relative formats with examples, `value` mentions zero-padded CIK, and `type` notes current limitation. This goes 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 opens with specific user queries ("What's new with X", "latest on Y") and clearly states the tool provides a change feed for a company. It enumerates the data sources (SEC EDGAR, GDELT→GNews, USPTO) and contrasts with the sibling entity_profile, 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 explicitly tells when to use this tool versus entity_profile: "Use entity_profile instead when you want the static profile…". It also provides guidance on the `since` parameter format and recommends typical values like "30d" or "1m".

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