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

Beyond the readOnly/non-destructive/closed-world annotations, it discloses meaningful behavior: the business model is inferred from inputs when not supplied, the activation score is deliberately omitted for services, connectivity and investment businesses, benchmarks are 'example' ranges rather than authoritative, and unscored figures are surfaced as gaps. It does not describe the response format or how conflicts between a supplied segment and the inferred one are resolved in the output, but the annotations already lower the disclosure burden.

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 core purpose is front-loaded in the opening clause, which is good, but the whole definition is one long comma-spliced sentence that piles secondary features (inferred business model, activation omission, feedback quotes) into the same breath. It is dense rather than wasteful, yet a short second sentence or bulleted clause would make the conditional logic far easier to parse.

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 and seven parameters, the description does the necessary work of telling the agent what comes back: per-dimension scores, benchmark ranges, priority actions, data gaps and answered feedback. What is missing is any indication of the return format or structure, which an agent would otherwise get from an output schema.

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 explains every parameter, including the enums and the sector-versus-target-market distinction. The description only restates the metric list and the business-model inference, adding little syntax or semantics beyond what the schema fields already contain.

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 states a concrete verb and resource: it takes product, target market and metrics and returns a scorecard that scores each PMF dimension against example benchmark ranges. It also defines scope precisely enough to separate this tool from siblings like retention_playbook or competitive_intel, and it spells out secondary outputs (priority actions, data gaps, feedback quotes).

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?

It gives a clear usage context – deciding whether you have product market fit and whether to scale – which is enough for an agent to know when to reach for it. It never states when NOT to use it or names an alternative sibling, so there is no explicit routing guidance.

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