Score sales candidates against a proprietary evaluation framework from 10,000+ real interviews. Two tools: generate custom interview scripts and score transcripts with ADVANCE/HOLD/PASS verdicts across 8 signal dimensions.
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Average 3.8/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one generates an interview script, the other scores a candidate from a transcript. No overlap or ambiguity exists between them.
Both tools follow a consistent pattern with the 'archetype_' prefix followed by a short verb ('prep', 'score'). This is coherent and predictable.
With only two tools, the server is on the thin side, but this is appropriate for a specialized interview workflow. It does not feel excessively sparse given the focused scope.
The server covers the complete lifecycle of the interview process: preparing a script and scoring the result. There are no obvious missing operations within its stated purpose.
archetype_prepAInspectGenerate 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.
| Name | Required | Description | Default |
|---|---|---|---|
| function | Yes | Function: sales, cs, marketing, bd, ops, or eng. | |
| role_type | Yes | 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. | |
| resume_text | Yes | Full resume or LinkedIn text. Not URLs. | |
| candidate_name | Yes | Name of the candidate | |
| additional_context | No | Optional context about the company and role |
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.
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.
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.
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.
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.
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.
archetype_scoreAInspectScore a candidate from an interview transcript. Returns a full evaluation with 8 trait scores, qualitative analysis, and ADVANCE/HOLD/PASS verdict across six functions: Sales, CS, Marketing, BD, Ops, and Engineering. Each function uses its own scoring framework with unique traits, weights, and anti-pattern detection.
| Name | Required | Description | Default |
|---|---|---|---|
| function | Yes | Function: sales, cs, marketing, bd, ops, or eng. | |
| role_type | Yes | Role type within the function. | |
| resume_text | No | Optional resume text | |
| candidate_name | Yes | Name of the candidate | |
| hiring_criteria | No | Optional specific requirements to evaluate | |
| transcript_text | Yes | Full interview transcript | |
| additional_context | No | Optional context |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It transparently discloses the output structure (8 trait scores, qualitative analysis, verdicts), the function-specific scoring frameworks, and anti-pattern detection. It does not mention side effects or prerequisites, but for a scoring tool this is reasonable. It adds significant context beyond the 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?
The description is concise at two sentences, front-loaded with the core purpose, and every sentence adds value. The first sentence states what it does and the output; the second explains function-specific behavior. No fluff or redundancy.
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?
Without an output schema, the description adequately explains return values (full evaluation, trait scores, analysis, verdicts) and the function-specific frameworks. It could be more explicit about what constitutes a 'full evaluation' or how the transcript is used, but given the tool's complexity, this is a solid description that covers key aspects.
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 some context by explaining that each function uses its own scoring framework, which helps clarify the 'function' and 'role_type' parameters. However, it does not directly elaborate on individual parameter semantics beyond what the schema already provides.
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 primary action ('Score a candidate from an interview transcript'), specifies the resource (candidate transcript), and details the output (8 trait scores, qualitative analysis, verdict across six functions). It distinguishes itself from the sibling archetype_prep by focusing on evaluation rather than preparation.
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 context is implied: use when you have an interview transcript and need a function-based candidate evaluation. However, it does not explicitly state when to prefer this over archetype_prep or any exclusions/alternatives. The sibling tool is not referenced, leaving some ambiguity in tool selection.
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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