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

Annotations confirm read-only, non-destructive, closed-world behavior, so safety needs no restatement. The description adds genuine behavioral value on top: sector is either chosen or inferred from inputs, questions adopt the sector's terminology, supplied hypotheses are preserved intact, and synthesis templates are bundled – all relevant for a tool with no output schema.

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?

Two sentences, front-loaded with the input→output contract before the qualitative guarantees. Dense but every clause carries information; only mild redundancy in re-listing schema params.

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?

For an 8-parameter tool with 5 enums and no output schema, the description covers input roles, inference fallbacks and the rough contents of the returned guide (questions, preserved hypotheses, synthesis templates). It does not describe guide structure or length, but nothing essential to invoking it correctly is missing.

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 enum semantics for industry, your_sector, business_model, product_complexity and interview_type are already documented in the schema, including the fallback inference behavior. The description restates rather than extends that, so the baseline 3 applies.

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?

States a concrete verb and resource: it takes interview_type/product_context/target_persona and returns an interview guide, and it names three shaping factors (interview type, sector, product complexity). An agent immediately knows this generates a research artifact rather than analyzing data. It does not contrast itself with siblings, though none of the listed siblings (competitive_intel, pmf_scorecard, etc.) overlap with interview-guide generation.

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?

Usage is only implied through the input names – the presence of interview_type with values like discovery/validation/churn signals this is for planning a customer interview. There is no explicit 'use this when' statement, no prerequisites, and no guidance on choosing between interview types or on when a different sibling tool would be better.

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