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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context: that label='Trend' returns a 'full deterministic list' and that search may return partial results, which implies a completeness guarantee beyond what annotations express. The openWorldHint=false also aligns with the deterministic-list claim, so no contradiction exists.

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?

Three sentences with no filler: the purpose, usage guidance, and exception case are each in their own sentence. The most important usage guidance (when to call with label='Trend') is front-loaded. It's slightly wordy with examples, but the examples earn their place by illustrating the query patterns for an agent.

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 list tool with fully-documented parameters (100% schema coverage) and no output schema, the description covers the main use case, the special deterministic case, and clearly distinguishes from search_graph. Minor gap: it doesn't explain the 'property' parameter's behavior beyond 'defaults vary by label,' but the schema covers this, and the complexity is moderate. An agent can call this tool 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 reinforces the label parameter semantics by naming the label values ('Brand', 'Location', 'Technology', 'Audience', 'RetailerType', 'Trend') and clarifying the special behavior of 'Trend'. It doesn't add new parameter semantics beyond the schema, but it doesn't need to — the schema descriptions are comprehensive.

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 verb ('List') with a clear resource ('brands, locations, technologies, audiences, or trends within a specific knowledge graph') and provides concrete example queries. It also distinguishes itself from search_graph ('To get a complete list of every trend in a graph... useful... where search may return partial results'), which differentiates it from sibling tools. This is a clear, specific purpose statement.

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 explicitly says to use this tool for exploring graph contents and contrasts it with search_graph when full deterministic lists are needed ('useful for industry-report graphs where search may return partial results'). This gives the agent explicit when-to-use and when-not-to-use guidance, naming the alternative tool.

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