Archetype
Server Details
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.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.7/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: archetype_prep generates interview scripts, while archetype_score evaluates interview transcripts. There is no overlap in functionality, and the descriptions explicitly differentiate their roles in the interview process.
Both tools follow a consistent naming pattern with the prefix 'archetype_' followed by a descriptive verb (prep, score). This uniform structure makes the tool set predictable and easy to understand.
With only two tools, the server feels thin for its domain of candidate evaluation across five revenue functions. While the tools cover preparation and scoring, there are likely gaps in the interview lifecycle, such as tools for scheduling, follow-up, or integration with other HR systems.
The tool set is severely incomplete for a candidate evaluation system. It lacks essential operations like managing candidate profiles, tracking interview stages, or providing analytics. The two tools only cover script generation and scoring, leaving significant gaps in the overall workflow.
Available Tools
2 toolsarchetype_prepAInspect
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.
| 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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It mentions anti-pattern detection and scoring calibration, hinting at analytical behavior, but does not disclose if it is read-only, requires authentication, or any side effects. The description adds some context but is insufficient for a complete behavioral profile.
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 only two sentences, first explaining purpose and second adding credibility. It is concise and front-loaded with the core function, but the second sentence could be more action-oriented. Still, 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?
Given the tool has 5 parameters (4 required) and no output schema, the description does not explain return values or output format. It provides high-level capability but not enough detail for an agent to understand the output structure or how results relate to the sibling tool archetype_score.
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?
The input schema has 100% description coverage, so the schema already documents parameters well. The description adds minimal meaning beyond the schema, just stating 'tailored to a specific candidate and role'. Baseline of 3 is appropriate.
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 across six specific functions. It also provides distinguishing details like '10,000+ real interviews' and 'function-specific frameworks', making the purpose highly specific and distinct from sibling tools.
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 generating interview scripts but does not explicitly state when to use this tool versus alternatives like archetype_score. No when-not-to-use guidance or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
archetype_scoreAInspect
Score 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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the output structure (trait scores, analysis, verdicts) but does not mention side effects, authentication needs, rate limits, or whether the tool is read-only.
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 two sentences, front-loaded with the core purpose, and provides essential output details without waste.
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 7 parameters, 4 required, and no output schema, the description adequately covers the output structure and function-specific scoring. However, it could elaborate on the verdict or anti-pattern detection details.
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 coverage is 100%, so the schema already describes each parameter. The description adds no additional semantics beyond the schema, such as format or constraints, resulting in baseline adequacy.
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 scores a candidate from an interview transcript and returns a full evaluation with 8 trait scores, qualitative analysis, and verdicts across six functions. It differentiates from the sibling 'archetype_prep' by focusing on scoring 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?
The description does not explicitly state when to use this tool versus alternatives like 'archetype_prep'. It implies usage for scoring from transcripts but lacks guidance on exclusions or prerequisites.
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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