profile_page_performance

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

With no annotations, the description fully discloses side effects (may disable cache, impacts memory/CPU), prerequisites (active Chrome tab, uses bandwidth), and return value. This is comprehensive behavioral transparency.

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 well-structured and front-loaded with key information. It is reasonably concise, though some sentences could be merged (e.g., side effects and prerequisites). Still, it is efficient and clear.

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 tool's complexity and the absence of an output schema, the description thoroughly explains return values, side effects, parameters, and usage context. An agent can fully understand how and when to invoke 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 coverage is 100%, so baseline is 3. The description does not add extra parameter semantics beyond the schema's own descriptions, which already explain constraints and interactions (e.g., action options, duration range, cache behavior).

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 records a performance trace, calculates Core Web Vitals (FCP, LCP, DCL, Load), and identifies Long Tasks. It distinguishes itself from siblings like get_performance_metrics by focusing on tracing and bottleneck identification.

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 advises using the tool for performance optimization, bottleneck identification, and cold start measurement. It also provides alternatives (browser DevTools, RUM), giving clear when-to-use and when-not-to-use guidance.

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