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

Annotations already declare readOnlyHint=true, so safety is covered. The description adds useful behavioral context beyond the schema: it explains the output shape (time series, one per classification id, each item with period timestamp and count), and clarifies that classifications are inferred from chat content. This is meaningful behavioral 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 three sentences with no filler. The main purpose is front-loaded, followed by output structure and parameter selection guidance. Every sentence adds value and the length is appropriate for the tool's simplicity.

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?

For a read-only analytics tool with only two parameters and a full output schema, the description covers what the tool does, what it returns, and how to choose the classification type. The input schema documents time_filter, and annotations cover the read-only nature, so nothing critical is missing.

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 both parameters are already thoroughly documented in the input schema. The description largely repeats the classification_type semantics from the schema rather than adding substantial new meaning. It meets the baseline but does not exceed 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 clearly states the tool's function: retrieving time series of trending classifications (topics or intents), with one series per classification id. It explicitly distinguishes between topic and intent, which differentiates it from sibling analytics tools focused on other dimensions like sentiment or active sessions.

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 gives clear usage context ('Use this to see how topical or intent demand shifts over time') and explains when to choose 'topic' versus 'intent'. It does not explicitly name alternative sibling tools or provide exclusion criteria, but the guidance is sufficient for selecting this tool.

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