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

Beyond the annotations (readOnly, idempotent), the description explains the fan-out to SEC EDGAR, GDELT/GNews fallback logic, USPTO soft-fail status, and return structure (grouped changes[], total_changes, citation URIs). This adds substantial behavioral context that annotations alone do not convey.

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 dense but well-organized: it front-loads user-intent phrases, then states the core function, then details backends and output. A few extra paraphrases ('news on Tesla recently') could be trimmed, but every sentence contributes to mapping user questions to tool capabilities, so it's appropriately sized for 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?

For a tool with three parameters and no output schema, the description covers all essential context: what sources are hit, fallback and failure behavior, output shape, and the alternative tool. The presence of strong annotations (read-only, idempotent) and detailed parameter descriptions means the description can focus on behavioral nuance, which it does thoroughly.

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 3. The description adds value by giving examples for `since` ('7d', '30d', '1y') and a recommendation ('Use "30d" or "1m" for typical monitoring'), plus clarifying that `value` can be a ticker or CIK. This goes beyond the schema's stated types.

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 sample queries ('What's new with X' etc.) then clearly defines the tool: 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It also distinguishes itself from entity_profile, making the purpose specific and 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?

Provides explicit usage guidance: 'Use entity_profile instead when you want the static profile ... regardless of window.' It also communicates the tool's scope via example queries and notes fallback behavior (GDELT->GNews), giving the agent strong direction on when to invoke it.

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