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

Beyond the readOnly/idempotent annotations, it discloses important runtime behavior: GDELT is preferred over GNews with fallback on rate-limit or 5xx, USPTO soft-fails due to API sunset, and the return shape (changes[] grouped by source, total_changes, pipeworx:// citation URIs). This is far richer than the annotation defaults and helps set expectations for external API failures.

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 every sentence serves a purpose: triggers, data sources, fallback behavior, parameter syntax, return structure, and alternative tool. It front-loads the most actionable information and keeps structural overhead low despite the multi-source 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?

Given the multi-source fan-out and no output schema, the description is remarkably complete: it explains the aggregation (structured changes[] grouped by source + total_changes), citation URIs, fallback logic, and failure modes. The complexity is high, but the description equips the agent with enough context to invoke correctly and interpret results.

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?

Although schema coverage is 100%, the description adds meaningful semantics: 'since' accepts ISO or relative shorthand with typical monitoring recommendation, 'value' gives ticker vs. zero-padded CIK examples, and 'type' is constrained to company. This goes beyond the schema's field descriptions by clarifying formats and usage patterns.

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 concrete query patterns ('What's new with X' / 'latest on Y') and then names a specific verb-resource pair: 'change feed for a company in the last N days'. It clearly differentiates itself by listing the data sources (SEC, GDELT/GNews, USPTO) and explicitly contrasts with entity_profile.

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 when-to-use guidance via natural-language triggers, explains the parallel fan-out behavior, and states a clear alternative: 'Use entity_profile instead when you want the static profile... regardless of window.' This gives the agent strong decision criteria beyond the schema.

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