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

Annotations declare readOnlyHint=true and idempotentHint=true, but the description adds crucial behavioral details: fans out in parallel, per-source fallbacks, PatentsView API sunset causing soft-fail, and the return structure (changes[] grouped by source, total_changes, pipeworx:// URIs). These go well beyond the boolean hints and are valuable for an agent invoking the tool.

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 that front-loads natural-language query examples, then efficiently packs in sources, date handling, fallbacks, and output format. Every sentence contributes information; there is no filler or redundancy.

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 high complexity (multiple data sources, fallbacks, failure modes) and the absence of an output schema, the description is remarkably complete. It covers inputs, source behavior, fallback triggers, the PatentsView sunset issue, and the output structure (changes[], total_changes, citation URIs), leaving little ambiguity for correct invocation.

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 covers 100% of parameters, so the description need not repeat them. However, it adds practical value with concrete examples of ISO dates vs relative shorthand ('7d', '30d', '3m', '1y') and recommends '30d' for typical monitoring, which helps an agent choose appropriate values beyond what the schema specifies.

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 starts with concrete query examples, then states it is a 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It names specific sources (SEC EDGAR, GDELT→GNews, USPTO) and explicitly differentiates from sibling tool 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 tells when to use this tool vs alternatives by stating 'Use entity_profile instead when you want the static profile...' It also explains fallback behavior (GDELT preferred, GNews on rate-limit/5xx) and the USPTO soft-fail, which helps the agent decide when and how to rely on the tool.

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