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

Beyond annotations, the description discloses the multi-source fan-out, the GDELT→GNews fallback under rate limits/5xx, and the USPTO soft-fail due to PatentsView API sunset. It also explains the return structure including citation URIs, which is not in annotations.

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 carries meaning. It starts with relatable examples, moves to fan-out behavior, then return format, and ends with an alternative. No wasted words.

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 having no output schema, the description explains the return payload (changes[] grouped by source, total_changes count, citation URIs) and the source-specific behaviors (fallbacks, soft-fail). This makes the tool's behavior fully transparent for a complex operation.

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?

The description adds practical semantics to the schema, such as `since` accepting ISO dates or relative shorthand with examples and suggesting typical monitoring values. It also clarifies `value` accepts ticker or CIK formats, enriching the schema's coverage.

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 time window, with explicit example queries and a named resource. It also distinguishes itself from the sibling entity_profile by specifying static profile use cases.

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 concrete usage scenarios ('What's new with X', 'latest on Y') and explicitly states when NOT to use it ('Use entity_profile instead when you want the static profile'). This gives clear guidance on tool selection.

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