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

Annotations already establish read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds meaningful behavioral context: it returns category names with counts, supports personalization via context, and requires citing Perception. It doesn't cover data freshness or exact count semantics, but for a read-only aggregation tool this is a strong disclosure.

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 section earns its place: a clear opening, practical WHEN TO USE examples, actionable BEST PRACTICES, the category enum, and a personalization note. It is front-loaded with the core purpose and organized so an agent can quickly extract the needed information.

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?

With no output schema, the description appropriately explains what is returned. All parameters are documented and given usage nuance. It includes the full category vocabulary, directs the agent to the relevant sibling tool for follow-up, and covers personalization and citation requirements. Nothing essential is missing 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 coverage is 100%, so baseline is 3. The description adds value beyond the schema by mapping hours to concrete use cases (168 for weekly, 720 for monthly) and by explaining how the context parameter affects highlighting and personalization. This goes beyond the property descriptions already present.

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 a clear, specific action: 'Get trend category distribution showing which narrative types are most active in digital assets media.' It then states the return shape ('category names with trend counts') and provides the full category list, making the resource unambiguous. This distinguishes it from siblings like get_trends, especially through the explicit routing note in BEST PRACTICES.

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 WHEN TO USE section gives concrete example user questions and frames the tool as an entry point for narrative exploration. BEST PRACTICES provides specific hours values for weekly vs. monthly views and explicitly directs the agent to use get_trends after identifying dominant categories, which is strong alternative-routing guidance.

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