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

The description adds valuable behavioral detail beyond the annotations by disclosing the parallel search-all-graphs default and the enriched output format ('trend data with cited evidence, source attribution, and lifecycle stage'). It does not mention rate limits, pagination, or what happens with invalid graph IDs, but those are not essential for a read-only search tool. Annotations declare readOnly and destructive false; the description is consistent with these, so no contradiction.

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 four tight sentences, each contributing a distinct, non-redundant piece of information: what it does, what it returns, its default behavior, and when to use it. There is no filler or re-statement of the tool name.

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 tool with 9 parameters and no output schema, the description covers the essential context: output structure, default search behavior, and representative use cases. It does not directly mention the 'compare' mode or the effect of skills/semantic parameters, but those are detailed in the schema and do not prevent correct high-level selection. The absence of an output schema is mitigated by the stated return format.

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 every parameter already has a meaningful description. The tool description does not add additional parameter-level insight beyond restating the graphId default, which is already present in the schema. The baseline of 3 applies because the schema carries the full weight of parameter semantics.

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 uses a specific verb and resource: 'Find trends, signals, and expert insights across 100+ curated knowledge graphs...' and immediately differentiates the tool by stating it returns curated trend data with cited evidence rather than 'generic web summaries'. It also names the covered domains, making the tool's scope unmistakable.

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 lists appropriate use cases: 'Use for market trends, competitor analysis, innovation signals, consumer behavior, cultural shifts, or any topic where curated expert intelligence outperforms web search.' It recommends the default behavior of searching all graphs when graphId is omitted, which guides invocation. It does not explicitly name an alternative tool or say 'don't use this when X', but the clear use-case list provides substantive guidance.

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