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

The description goes well beyond the readOnly/idempotent annotations by detailing the multi-source fan-out, fallback logic, date parsing (ISO or relative shorthand), and the grouped return structure with total_changes count and pipeworx:// citation URIs. It also notes the PatentsView API sunset, setting expectations for a soft-failure mode.

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?

Despite being longer than average, every sentence is information-dense. It is front-loaded with real user queries, then systematically covers sources, parameters, fallbacks, and return shape. There is no 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 complexity (three external sources, fallbacks, multiple date formats, no output schema), the description is remarkably complete. It covers the return shape (changes[] grouped by source, total_changes, citation URIs), the alternative tool, and the window semantics, enabling an agent to invoke it correctly without additional context.

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?

While the schema already covers all three parameters, the description adds meaning: it gives concrete examples of relative vs. ISO dates, recommends '30d' or '1m' for typical monitoring, and clarifies that value can be a ticker or zero-padded CIK. These enrich the schema definitions with practical guidance.

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 user intents ('What's new with X' / 'latest on Y'), then states the tool is a change feed for a company in a recent time window that fans out to SEC EDGAR, GDELT/GNews, and USPTO. It clearly distinguishes itself from the sibling entity_profile tool, which is positioned as the alternative for static profiles.

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 explicitly tells when to use this tool vs. entity_profile ('Use entity_profile instead when you want the static profile...'), explains the time-window semantics, and even provides fallback behavior (GDELT preferred, GNews when rate-limited or 5xx; USPTO soft-fails after API sunset). This gives the agent clear decision criteria.

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