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

Beyond the readOnlyHint=true annotation, the description discloses the cost behavior: the cached report is free, otherwise it spends one fresh-audit slot. This resource-consumption detail is valuable and not present in 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 structured into three purposeful parts: what it returns, when to use it versus lekta_audit, and cost behavior. Every sentence adds distinct value and nothing is redundant.

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?

With no output schema, the description compensates by specifying the JSON report contents: every layer, every check, status, evidence, and fix text. It also covers usage context, alternatives, and cost, which is sufficient for correct invocation.

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 only parameter, url, is fully described in the input schema with a clear example. The description does not add parameter-specific detail, but schema coverage is 100%, so the schema already carries the burden.

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 explicitly states this tool returns the same measurement as lekta_audit but as a complete structured JSON report with every layer, check status, evidence, and fix text. This clearly distinguishes it from the sibling lekta_audit tool.

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 explicit usage guidance: use it when a program consumes the result or when lekta_audit omits a needed field, and do not use it for a quick human-readable verdict. It also names lekta_audit as the alternative, making the decision unambiguous.

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