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

Annotations already declare the tool read-only, idempotent, and non-destructive, so the description does not need to restate safety. The added behavioral context about sources in 'error' or 'no_data' being excluded from figures and connections.note providing remediation guidance is valuable beyond the annotations.

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 front-loaded with the tool's purpose and return contents, followed by an important caveat and a compact Args list. Every sentence earns its place, with no filler or repetition of schema details already visible in the input schema.

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?

Without an output schema, the description provides a good inventory of the returned sections plus a nuanced data-quality caveat. It does not mention the user_intent parameter in the Args block, though the schema itself documents it, and it could describe more about the shape of time series or metrics, but overall it is sufficient for selecting and invoking the 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?

Schema description coverage is only 17%, but the Args section compensates thoroughly: it documents brand_id as required, explains range_preset options with a default, defines start_date and end_date as required for custom ranges with YYYY-MM-DD format, and enumerates compare_to choices. This adds real meaning beyond the schema fields.

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 retrieves the crawler dashboard overview for a brand and enumerates what it includes (metrics, time series, platform breakdown, top pages, recent preview). It does not explicitly differentiate from sibling tools like get_crawler_detail or get_crawler_live, but the overview scope is evident from the content.

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 context about what the tool returns and even instructs the agent to mention missing sources to the user, but it never explicitly states when to choose this tool over alternatives like get_crawler_detail or get_crawler_pages. Usage is implied by the dashboard-overview framing rather than spelled out.

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