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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description goes beyond by detailing the output structure (structured fields + markdown blob + caller_reasoning block) and the cooperative sampling mechanism. It explains that the caller_reasoning block contains a prompt that the agent can execute to personalize the recommendation, and that if unable, rendering markdown as-is works. No contradictions with 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 detailed but well-structured, with the key usage statement front-loaded. Each part provides necessary guidance: usage, output format, cooperative sampling, and fallback behavior. While lengthy, every sentence adds value, and it avoids redundancy with the schema. Minor reduction could improve conciseness, but it remains effective.

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 presence of an output schema, the description adequately covers all necessary context: when to use, what the tool returns (structured + markdown, caller_reasoning), how to interpret cooperative sampling, and fallback instructions. No gaps are apparent for an agent selecting this tool. The combination of description and schema provides full context.

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 100%: both 'url' and 'include_vertical' have descriptions. The description adds minimal extra meaning ('we'll scan + detect vertical' for url and 'Include vertical context block (default true)' for include_vertical), which is already in the schema. Baseline is 3, and the description does not significantly enhance 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 clearly states the specific purpose: 'Use this when displaying scan output to a human via chat'. It identifies the verb (summarize/render), the resource (scan output), and the audience (humans). It differentiates from sibling tools like run_site_audit and scan_site by focusing on human-readable formatting rather than raw data.

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 states the usage context ('when displaying scan output to a human via chat') and provides guidance for handling the output (render markdown or execute the prompt from caller_reasoning). It also mentions that chat clients that can't render React get the same story, implying when this tool is appropriate. However, it doesn't explicitly list alternative tools for non-chat scenarios.

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