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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is covered. The description adds valuable context about the nature of the data (real projects, measured outcomes, live instrumentation) and what the results include (what was built, link to live site). It doesn't describe pagination or ordering, but the schema covers limit and category, so the description adds meaningful behavioral context beyond 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 three sentences, front-loaded with the core value proposition (real projects, measured outcomes), then usage examples, then a data-quality note. Every sentence earns its place with no fluff.

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 read-only list tool with two optional parameters and no output schema, the description is quite complete. It covers what the tool returns, the quality of the data, and when to use it. It doesn't mention ordering, pagination beyond the limit parameter, or whether results are sorted by relevance, but these are minor gaps given the simplicity of the tool.

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 already documents both parameters (limit and category). The description doesn't add much beyond the schema, but it does mention category examples in the schema itself. The description's mention of 'measured outcomes' and 'live site' gives context for what the results contain, but not much about parameter semantics. Baseline 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?

The description clearly states the tool returns real client projects with measured outcomes, including specific metrics (traffic lift, conversion change, Lighthouse scores) and links to live sites. It distinguishes itself from siblings by focusing on portfolio proof with measured outcomes, which is unique among the listed tools.

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 provides example use cases ('show me their work', 'do they have proof', 'have they built anything like mine'), which helps an agent decide when to invoke this tool. It also clarifies that KPIs marked 'measured' come from live instrumentation, not estimates, setting expectations for the data quality.

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