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AI Applyd

by io.github.whateverneveranywhere

Generate Interview Questions

aiapplyd_generate_interview_questions
Read-only

Produce interview preparation for a specific role and company: company insights, the questions this role is asked with approach guidance, STAR scenarios drawn from the posting, talking points, questions to ask the interviewer, and salary negotiation prep. Requires a connected AI Applyd account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_titleYesTitle of the position (e.g. "Senior Software Engineer")
company_nameYesName of the company
job_descriptionNoFull text of the job description (optional but recommended for better results)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • removedInput schema / properties / locale
      Removed value: -{
-  "description": "Language for the human-readable parts of the answer: en, es, de, fr or pt-BR. Defaults to the language this connection was opened in. Machine fields (ids, URLs, scores) never change.",
-  "enum": [
-    "en",
-    "es",
-    "de",
-    "fr",
-    "pt-BR"
-  ],
-  "type": "string"
-}
  2. Changed1 schema field changed
    • addedInput schema / properties / locale
      Added value: +{
+  "description": "Language for the human-readable parts of the answer: en, es, de, fr or pt-BR. Defaults to the language this connection was opened in. Machine fields (ids, URLs, scores) never change.",
+  "enum": [
+    "en",
+    "es",
+    "de",
+    "fr",
+    "pt-BR"
+  ],
+  "type": "string"
+}
  3. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds valuable context beyond annotations by stating the account requirement and that STAR scenarios are derived from the job posting, which clarifies the tool's behavior and data source.

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 efficiently lists all deliverable types and the account prerequisite. Every phrase adds value, with no redundant or filler content.

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

Completeness4/5

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

The description thoroughly covers the tool's output sections and prerequisites, which is important given no output schema. However, it leaves ambiguity about how the tool behaves when job_description is omitted (since it's optional but central to STAR scenarios), preventing a perfect score.

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 all three parameters fully documented. The description adds slight extra meaning by linking job_description to STAR scenarios ('drawn from the posting') and mentioning 'specific role and company,' but does not substantially expand on the schema's parameter descriptions. Baseline 3 is appropriate.

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 uses a specific verb ('Produce') and identifies the resource ('interview preparation for a specific role and company'). It enumerates distinct output components (company insights, questions, STAR scenarios, etc.), making it clearly distinct from sibling tools like cover letter generation or resume optimization.

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

Usage Guidelines4/5

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

The description clearly conveys when to use the tool (preparing for an interview at a specific company/role) and includes a prerequisite (connected AI Applyd account). It does not explicitly name alternatives or exclusions, but the specificity of the output and the sibling tool names imply appropriate use cases.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool maps cleanly to a distinct job-search action: analyze a posting, apply, build/optimize/score/translate resumes, generate cover letters/interview prep, search jobs, and update preferences. The only adjacent pair, score_resume and optimize_resume, is clearly separated by assessment versus rewrite.

Naming Consistency5/5

All tools share a consistent aiapplyd_ prefix and follow an actionable verb_noun pattern: analyze_job_description, generate_cover_letter, score_resume, update_job_preferences. No style mixing or vague generic names are present.

Tool Count5/5

Ten tools is well-scoped for a job-application assistant, covering the main user journeys without redundancy or bloat. Each tool has a clear, non-overlapping responsibility.

Completeness4/5

The tool surface covers the core lifecycle: preferences, job search, resume analysis and optimization, PDF generation, cover letters, auto-apply, and interview prep. Minor gaps exist around listing saved resumes/cover letters and tracking submitted applications, but these are workable via the connected account.

Resources