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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses key behavioral traits: fan-out to multiple sources, fallback logic, soft-failure of the USPTO feed due to API sunset, and the return structure (changes[], total_changes, citation URIs). This gives the agent a realistic model of what the tool will do.

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 longer than ideal but well-structured: it opens with natural-language examples, states the core function, enumerates sources, explains input syntax, specifies return format, and closes with an alternative tool. Every section serves a purpose, though the list of example queries could be trimmed without losing essential meaning.

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 lack of an output schema, the description clearly states the return shape (changes[] grouped by source, total_changes count, pipeworx:// citation URIs). It also covers edge cases like GNews fallback and USPTO soft-fail, and differentiates from entity_profile. This is complete for a tool with this complexity.

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 parameters are already fully described. The description adds only a minor usage recommendation for 'since' ('Use "30d" or "1m" for typical monitoring') and restates the ISO/relative shorthand that the schema already provides. This does not significantly expand parameter understanding beyond the schema.

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 clearly defines the tool as a change feed for a company over a recent time window, with specific verbs like 'change feed' and 'Fans out to'. It distinguishes itself from sibling entity_profile by explicitly naming the alternative and contrasting the use case (dynamic changes vs. static 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?

The description provides explicit when-to-use guidance through example natural-language queries, and explicitly says 'Use entity_profile instead when you want the static profile...'. It also offers practical advice such as 'Use "30d" or "1m" for typical monitoring' and explains the fallback behavior (GDELT preferred, GNews when rate-limited).

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