customer_interview_kit

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

No annotations are provided, so the description carries the full burden. It transparently lists what the generated kit contains (e.g., 'insight synthesis template', 'cross-interview pattern analysis framework'), but does not disclose potential side effects, prerequisites, limitations, or any behavioral constraints beyond generating content. Lacks explicit statements about read-only nature or data handling.

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 a single sentence but well-structured, front-loading the core purpose ('Generate complete interview kit') followed by a detailed but efficient list of included elements. No redundant words, though it reads as a slightly long run-on; it earns its length by specifying multiple deliverables.

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 no output schema and no annotations, the description must explain what the tool returns. It does so thoroughly by listing six distinct output components. The parameters are fully documented in the schema, so the description complements them well. However, it could be improved by noting the relationship between inputs (e.g., 'customer_segment') and the generated content.

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 baseline is 3. The description adds no additional meaning to parameter semantics; it focuses on output components rather than explaining how parameters like 'research_objective' or 'interview_type' should be set or interact. The schema already provides clear parameter descriptions.

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 the tool's purpose: 'Generate complete interview kit' and enumerates specific deliverables (5-phase script, question bank, follow-up prompts, etc.). This distinguishes it from sibling tools like pmf_scorecard or competitive_intel, which focus on different research or analysis activities.

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 use for customer research and interviews but provides no explicit guidance on when to use this tool versus alternatives. It does not mention exclusions or conditions, leaving the agent to infer applicability from the phrase 'Customer Research Framework'.

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