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

Annotations already establish readOnlyHint=true and openWorldHint=false, and the description adds meaningful behavioral details: results are normalized (lowercased/trimmed) and ranked by occurrence count. It also positions the tool as a high-level analytics entry point, which informs the agent's expectations about granularity.

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?

Two sentences with no filler: the first delivers the core result and normalization behavior, the second explains why and how to use it. Every sentence earns its place and the most important information is front-loaded.

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?

Given the low parameter count, high schema coverage, output schema presence, and read-only annotations, the description is complete. It adds the key non-obvious context—normalization, ranking, and the recommended pairing with read_sessions—leaving no critical gap for an agent to call it 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 description coverage is 100%, so the schema fully documents both 'limit' and 'time_filter' parameters, including defaults and formats. The description adds no parameter-specific meaning beyond what the schema already provides, so the baseline of 3 is appropriate.

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?

States a specific verb and resource ('get top searches') and describes exactly what is returned: normalized, lowercased, trimmed user search queries ranked by occurrence count. The normalization detail adds precision and helps distinguish this from sibling analytics tools like get_top_clicked_urls or get_top_cited_sources.

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?

Explicitly says when to use this tool ('discover dominant user intents and content gaps') and provides a concrete complementary workflow: pair with read_sessions(search_query=...) to inspect specific sessions. This is clear, actionable guidance that routes the agent appropriately.

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