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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the description doesn't need to repeat safety profile. It adds valuable behavioral detail beyond annotations: fan-out to multiple sources in a single call, GDELT→GNews fallback on rate limits/5xx, USPTO soft-failure due to PatentsView API sunset, and `since` format details. These disclosures help the agent anticipate side effects and edge cases.

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 average but every sentence contributes meaning: from usage examples to source details, parameter guidance, return shape, and an alternative. It is front-loaded with the core purpose and structured logically. Slightly verbose due to the enumeration of sources and fallback logic, but nothing is redundant.

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 (multi-source fan-out, fallbacks, soft-fail) and lack of an output schema, the description fully covers what is returned: structured changes[] grouped by source, total_changes count, and pipeworx:// citation URIs. It also addresses the entity_profile alternative, making the tool self-contained for an agent to decide and invoke 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?

Schema coverage is 100%, so the baseline is 3. The description adds extra semantics by giving concrete relative shorthand examples for `since` ('7d', '30d', '3m', '1y') and a recommendation to use '30d' or '1m' for typical monitoring, which goes beyond the schema's generic description. It also clarifies the `value` parameter implicitly through the entity_profile contrast (ticker/CIK), though the schema already covers it.

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 natural-language queries and then states the core function: a change feed for a company over a time window, in one parallel call. It distinguishes itself from the sibling `entity_profile` by explicitly contrasting dynamic changes with a static profile, making the tool's 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 provides explicit context for when to use the tool (e.g., 'What's new with X', 'latest on Y') and gives a clear alternative: 'Use entity_profile instead when you want the static profile'. This satisfies the when/alternative criteria, and the fallback behavior (GDELT→GNews) further clarifies operational expectations.

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