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

The description adds useful behavioral context beyond the annotations: it is based on local real history, computes several named metrics, and returns insufficient_history when history is insufficient. Annotations already signal read/write and destructiveness, so the description does not need to restate those. No contradiction arises with the 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 a single dense sentence that front-loads the core purpose, partitions responsibilities with the sibling, and includes the key edge-case return. Every clause earns its place with no redundancies or filler.

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 three parameters and no output schema, the description covers the essential behavior, the sibling boundary, and the failure condition. It could be more explicit about the shape of a successful return, but the listed metrics strongly imply what the result contains, and the schema handles parameter details.

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 description coverage is 100%, so all three parameters (keyword, refresh, platforms) are already documented in the schema. The description adds no parameter-specific meaning beyond that, which meets the baseline expectation but does not elevate 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 states a specific purpose: computing lifecycle, velocity, persistence, diffusion, source reliability, and confidence from local real history. It also explicitly distinguishes itself from professional_intelligence by listing the use cases the sibling covers. This makes the tool's role clear and differentiates it from its siblings.

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?

There is explicit routing guidance: brand entities, media, anomalies/predictions, alerts, and reports should use professional_intelligence. It also states the insufficient-history return condition. However, it does not explicitly state positive triggers for when to choose this tool over other trend-related siblings, so the when-to-use guidance is strong but not fully comprehensive.

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