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

The description adds substantial behavioral context beyond annotations: multi-source fan-out, fallback logic (GDELT → GNews), API sunset status (USPTO soft-fail), and return format structure (changes grouped by source, total_changes count, pipeworx:// URIs). The annotations (readOnlyHint, idempotentHint) are consistent and reinforced by the description.

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 front-loaded with query examples and structured into sources, parameters, and sibling comparison. It is slightly verbose but every sentence adds value; no redundancy. The length 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?

Despite the tool's complexity (multiple sources, fallback, sunset, no output schema), the description covers all critical aspects: input parameter semantics, output structure, source behavior, and alternative tool. It provides complete guidance for an AI agent to understand and invoke the tool correctly.

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?

All three parameters have descriptions in the schema (100% coverage). The description adds practical usage details: 'since' accepts ISO date or relative shorthand with examples, recommends '30d' or '1m', and clarifies that 'value' can be a ticker or zero-padded CIK. This goes slightly beyond schema but not dramatically.

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 begins with specific query patterns ('What's new with X', 'latest on Y'), then clearly states the tool returns a change feed for a company in a recent window, fanning out to SEC EDGAR, GDELT/GNews, and USPTO. It distinctly differentiates from sibling entity_profile by stating when to use that instead, achieving high specificity and resource identification.

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 says when to use the tool (for 'What's new' queries with a time window) and when not to (use entity_profile for static profile regardless of window). It also provides fallback behavior for news sources, giving clear context for selection among sibling tools.

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