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

Annotations already establish the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral nuance beyond that by disclosing that the Trend label returns a full deterministic list, which can differ from open-ended search results. This gives the agent a useful expectation about completeness and determinism.

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 two focused sentences with concrete examples and front-loaded imperative phrasing. Every sentence earns its place: first the purpose, then usage context, then a valuable edge-case behavior.

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?

The description covers purpose, intended usage, and a special-case behavior for the Trend label, which is enough for an agent to invoke the tool in most cases. It could be slightly more complete by explicitly naming the sibling search_graph and describing what to do when the requested label is unavailable, but those gaps are not significant given the rich schema.

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 input schema already covers all parameters with rich descriptions, so the baseline is 3. The description adds meaningful label-specific guidance: setting label to 'Trend' returns a complete, deterministic list. That is a useful semantic addition not present in the schema itself.

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 a specific action: list label values within a specific knowledge graph. It enumerates what kind of values can be listed and supports it with concrete example queries, which makes the tool's purpose unambiguous and distinct from a general graph-search tool.

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, including example exploratory queries and a specific special case: calling with label="Trend" to get a full deterministic list. It does not explicitly name an alternative tool like search_graph, but it hints that search may return partial results and that this tool should be used when a complete list is needed.

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