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

The description adds rich behavioral context beyond annotations: it discloses cost (~USD 0.03 per seed), external dependency on DataForSEO, and a specific degradation reason ('spend-limit'). This is critical for an agent to understand side effects and failure modes, far exceeding what the readOnlyHint and openWorldHint provide.

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 tight sentences with zero waste, front-loading the tool's purpose, then key parameter behavior, then cost and failure modes. Every sentence earns its place.

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 no output schema and 3 parameters, the description covers purpose, cost, dependency, and failure states well. It omits whether the suggestions are persisted or returned directly, and the seed parameter's expected format remains unspecified.

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 only 33%, with 'keep' lacking a description. The tool description clarifies that keep=true merges into the topic bank, compensating for that gap. However, the seed parameter is undocumented in both schema and description, leaving its format or expected input unclear.

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 verb and resource: it suggests related questions from a seed using semantic keywords and Google suggestions. It distinguishes itself from siblings like add_topics (which just adds) and research_topic by naming its source methodology.

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 'keep=true' instruction implies when to use the tool for merging versus not. However, there is no explicit guidance on when to use suggest_topics versus siblings like propose_topics or research_topic, which likely fill similar roles.

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