Archetype

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

With no annotations provided, the description carries the full burden. It adds valuable methodological context ('10,000+ real interviews', 'anti-pattern detection', 'scoring calibration') indicating output quality and content traits, but omits operational behaviors like idempotency, side effects, storage persistence, or rate limits.

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 efficiently structured sentences with zero waste. The first sentence front-loads the core action and scope; the second provides supporting credibility context. Every sentence earns its place.

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?

Adequate but with clear gaps. Given no output schema exists, the description should ideally specify the return format (structured JSON vs plain text script) and whether the generated script includes scoring rubrics or just questions. Without annotations, behavioral disclosure is also incomplete.

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 mentions 'tailored to a specific candidate and role' which aligns with the parameters but does not add syntax details, format constraints, or examples beyond what the schema already provides (e.g., schema already specifies 'Not URLs' for resume_text).

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 uses specific verb 'Generate' and resource 'custom interview script', clearly defining the scope across five revenue functions. However, it lacks explicit differentiation from sibling tool 'archetype_score' (e.g., stating this is for preparation while the sibling is for evaluation).

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?

No explicit guidance on when to use this tool versus alternatives, prerequisites (e.g., having a resume ready), or when-not-to-use scenarios. The agent must infer usage solely from the 'prep' naming convention and the sibling 'archetype_score'.

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

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

No annotations provided, so description carries full burden. It discloses key behavioral traits: returns structured evaluation with 8 trait scores, qualitative analysis, and ternary verdict. Uniquely explains that each function employs distinct scoring frameworks with 'unique traits, weights, and anti-pattern detection'—critical context for an evaluation tool. Missing operational characteristics (idempotency, data persistence, rate limits).

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 sentences, zero waste. First sentence establishes core purpose. Second details output structure and scope. Third explains behavioral variation across functions. Information-dense with no filler, well-structured with purpose front-loaded.

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?

Compensates well for missing output schema by detailing return structure (8 trait scores, qualitative analysis, verdict). Explains domain context (revenue functions) and evaluation methodology (anti-patterns). With 7 parameters and 100% schema coverage, input side is well-handled. Lacks only operational metadata (error handling, idempotency) to be complete.

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%, establishing baseline 3. Description adds marginal semantic value by framing the 'function' parameter values as 'revenue functions' and explaining that 'role_type' selections trigger different scoring frameworks with unique weights and anti-patterns. Does not add syntax details or parameter interaction rules beyond schema.

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?

Description opens with specific verb 'Score' and clear resource 'candidate from an interview transcript'. It distinguishes scope by specifying five revenue functions (Sales, CS, Marketing, BD, Ops) and detailed output format (8 trait scores, ADVANCE/HOLD/PASS verdict), clearly differentiating from sibling 'archetype_prep' which likely handles pre-interview preparation rather than post-interview evaluation.

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?

Implies temporal context through 'interview transcript' (suggesting post-interview use), but lacks explicit when-to-use guidance versus sibling 'archetype_prep' or prerequisites. No mention of whether to use this for initial screening vs final evaluation, or when to prefer one function/role_type over another.

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