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.
Claiming proves that you control this connector and unlocks listing details, thumbnails, health checks, and analytics. It does not change or interrupt the running server.
Complete one method below. Compare the methods and read troubleshooting steps.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
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.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, bound to the signed-in Glama account, and expire after seven days. They contain no email address or other personal information. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
