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

The annotations already establish that this is a read-only, non-destructive operation. The description adds genuinely useful behavioral context beyond annotations: searching ALL accessible graphs in parallel when graphId is omitted, the curated-not-generic nature of results, and the lifecycle-stage classification. No contradiction with the readOnlyHint or openWorldHint annotations was identified.

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 compact and front-loaded. Each sentence carries real content: what the tool finds, what it returns and how it differs from web search, and what the default behavior should be. There is no filler or redundant repetition of the tool name or schema details.

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?

Even though there is no output schema, the description explains the qualitative nature of the output (trends with evidence, source attribution, lifecycle stage) and the recommended invocation pattern. The input schema covers parameter semantics, and the annotations cover safety, so the description closes the remaining contextual gaps for selecting and invoking 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?

The input schema already documents each parameter well, with 100% coverage. The description adds only a recommendation about omitting graphId and the general search-scope behavior; it does not materially enhance the semantics of individual parameters beyond re-stating what the schema already says.

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 leads with a specific verb and resource: 'Find trends, signals, and expert insights across 100+ curated knowledge graphs.' It further clarifies the output type (trend data with cited evidence, source attribution, lifecycle stage) and explicitly contrasts itself with generic web summaries, which helps an agent distinguish it from siblings like get_validated_trends or get_node.

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?

It provides clear use contexts: market trends, competitor analysis, innovation signals, consumer behavior, cultural shifts. It also recommends omitting graphId to search all graphs. However, it does not explicitly name sibling tools as alternatives or state when not to use this tool, stopping it short of full routing guidance.

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