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Archetype

by vc.swell.archetype
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archetype_prep

Generate a custom interview script tailored to a specific candidate and role across six functions: Sales, CS, Marketing, BD, Ops, and Engineering. Built on 10,000+ real interviews with function-specific frameworks, anti-pattern detection, and scoring calibration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
functionYesFunction: sales, cs, marketing, bd, ops, or eng.
role_typeYesRole type. Sales: ae/enterprise. CS: csm/enterprise_csm. Marketing: marketing_mgr/marketing_leader. BD: bd_mgr/bd_leader. Ops: ops_mgr/ops_leader. Engineering: eng_early/eng_senior.
resume_textYesFull resume or LinkedIn text. Not URLs.
candidate_nameYesName of the candidate
additional_contextNoOptional context about the company and role

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • changedInput schema / properties / function / description
      Previous value: -"Revenue function: sales, cs, marketing, bd, or ops."New value: +"Function: sales, cs, marketing, bd, ops, or eng."
    • changedInput schema / properties / function / enum
      Previous value: -[
-  "sales",
-  "cs",
-  "marketing",
-  "bd",
-  "ops"
-]New value: +[
+  "sales",
+  "cs",
+  "marketing",
+  "bd",
+  "ops",
+  "eng"
+]
    • changedInput schema / properties / role_type / description
      Previous value: -"Role type. Sales: ae/enterprise. CS: csm/enterprise_csm. Marketing: marketing_mgr/marketing_leader. BD: bd_mgr/bd_leader. Ops: ops_mgr/ops_leader."New value: +"Role type. Sales: ae/enterprise. CS: csm/enterprise_csm. Marketing: marketing_mgr/marketing_leader. BD: bd_mgr/bd_leader. Ops: ops_mgr/ops_leader. Engineering: eng_early/eng_senior."
    • changedInput schema / properties / role_type / enum
      Previous value: -[
-  "ae",
-  "enterprise",
-  "csm",
-  "enterprise_csm",
-  "marketing_mgr",
-  "marketing_leader",
-  "bd_mgr",
-  "bd_leader",
-  "ops_mgr",
-  "ops_leader"
-]New value: +[
+  "ae",
+  "enterprise",
+  "csm",
+  "enterprise_csm",
+  "marketing_mgr",
+  "marketing_leader",
+  "bd_mgr",
+  "bd_leader",
+  "ops_mgr",
+  "ops_leader",
+  "eng_early",
+  "eng_senior"
+]
  2. Changed1 schema field changed
    • changedInput schema / properties / function / description
      Previous value: -"Revenue function: sales, cs, marketing, bd, or ops. Defaults to sales."New value: +"Revenue function: sales, cs, marketing, bd, or ops."
  3. Changed1 schema field changed
    • changedInput schema / required
      Previous value: -[
-  "candidate_name",
-  "role_type",
-  "resume_text"
-]New value: +[
+  "function",
+  "candidate_name",
+  "role_type",
+  "resume_text"
+]
  4. First observed

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It mentions methodology and features like anti-pattern detection and scoring calibration, but fails to disclose output format, side effects, authentication needs, or whether data is stored. This is a significant gap for a generation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that clearly names the action, target, and scope. It packs relevant details (six functions, data scale, frameworks) without unnecessary repetition or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 5 parameters and no output schema, the description should explain what the tool produces. It mentions an 'interview script' and features but does not detail the return format or structure. The description is adequate for understanding the tool's purpose but not fully complete for a tool with no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with detailed descriptions for all parameters including enums and role mappings. The description does not add meaning beyond the schema, but the baseline of 3 applies because the schema already fully documents the parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates a custom interview script tailored to a candidate and role, and enumerates six functions. This specific verb+resource combination makes the purpose unambiguous and implicitly distinct from the sibling archetype_score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for creating interview scripts but does not explicitly state when to use this tool versus archetype_score or any other alternative. No exclusions or alternative scenarios are given.

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

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