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

With no annotations supplied, the description carries the full behavioral burden, and it discloses nothing: no mutation semantics, no required inputs, no return format, no side effects. The only concrete statement ('works in any LLM chatbot') describes the host environment, not the tool's behavior.

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?

Three short sentences, but they are entirely promotional filler ('Save hours every week', 'battle-tested', 'No setup') with no front-loaded statement of function. The space is used on persuasion rather than on information an agent needs.

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?

The tool is simple (one optional param, no output schema), so a short description could suffice—but this one omits the single essential fact: what it rewrites and for whom. Compounding the gap, the restaurant/brand-growth framing conflicts with the perf-* naming pattern shared by its siblings, leaving the agent with a misleading rather than merely incomplete definition.

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 single optional 'query' parameter is fully described in the schema (100% coverage), including that it is optional and scopes the analysis. The description adds no meaning beyond that, so the baseline of 3 for high schema coverage applies. It neither clarifies nor misrepresents the parameter.

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 never states what operation the tool performs on what resource. 'Rewriting' only appears in the tool name, and the body instead advertises a 'prompt and operating playbook for restaurant/brand growth', which has no evident connection to the perf-* family (audit/check/gen/polish/rewrite/sort/summary/tpl/write). An agent cannot determine from this text whether it rewrites performance reviews, performance reports, or brand copy.

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?

There is no when-to-use guidance and no differentiation from the eight sibling perf-* tools (perf-polish-tool, perf-write-tool, perf-gen-tool, etc.). 'No setup, works in any LLM chatbot' is a marketing claim, not a condition for selecting this tool over an alternative.

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