lcp_optimization

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

No annotations are present, so the description must carry the full burden. It describes the outputs (element identification, measurements, suggestions) but does not disclose behavioral traits such as whether the tool modifies state, requires network access, or has resource implications. The description is adequate but could be more explicit about side effects.

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 with no redundant information. It front-loads the purpose and efficiently covers what the tool does and what it returns. Every sentence contributes value.

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 there is no output schema, the description adequately explains what the tool returns (element ID, measurements, suggestions). The single parameter is well-documented in the schema. The description is complete for the tool's function and 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?

The input schema covers the single parameter 'url' with a clear description. The tool's description does not add additional meaning beyond the schema; it just sets context. Since schema coverage is 100%, a 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?

The description clearly states it is a 'Deep Largest Contentful Paint (LCP) analysis' and enumerates specific capabilities: identifying the LCP element, measuring TTFB, resource load time, render delay, and providing optimization suggestions. This is a specific verb-resource combination that distinguishes it from sibling audit tools like 'performance_audit' or 'lighthouse_audit'.

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 implies usage for improving LCP performance and mentions a threshold (2.5s), but does not explicitly state when to use this tool versus alternatives or when not to use it. No exclusion or comparison to siblings is provided, leaving the agent to infer context.

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