AI Applyd

Generate Interview Questions

aiapplyd_generate_interview_questions

Generate tailored interview prep from a job title and company or application ID: likely questions, STAR scenarios, talking points, and salary negotiation guidance.

Instructions

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. Pass application_id to prep for a job the user applied to (no new saved job), or job_title and company_name for any other job. Requires a connected AI Applyd account. Uses the user's AI credits, and saves the job to their account if it is not saved yet. Do not use it for a cover letter; use aiapplyd_generate_cover_letter.

Input Schema

TableJSON Schema

Name	Required	Description
`job_title`	No	Title of the position (e.g. "Senior Software Engineer"). Pass with company_name, or pass application_id instead.
`company_name`	No	Name of the company. Pass with job_title, or pass application_id instead.
`application_id`	No	Prep for the job of this application (an id from aiapplyd_get_applications). Replaces job_title and company_name.
`job_description`	No	Full text of the job description (optional but recommended for better results)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`jobId`	Yes
`prepUrl`	Yes
`jobTitle`	Yes
`companyName`	Yes
`questionsToAsk`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedv1.8.0Sep 25, 2026

removedInput schema / additionalProperties
```
Removed value: -false
```

addedInput schema / properties / application_id

Added value: +{
+  "description": "Prep for the job of this application (an id from aiapplyd_get_applications). Replaces job_title and company_name.",
+  "exclusiveMinimum": 0,
+  "maximum": 9007199254740991,
+  "type": "integer"
+}

changedInput schema / properties / company_name / description

Previous value: -"Name of the company"New value: +"Name of the company. Pass with job_title, or pass application_id instead."

changedInput schema / properties / job_title / description

Previous value: -"Title of the position (e.g. \"Senior Software Engineer\")"New value: +"Title of the position (e.g. \"Senior Software Engineer\"). Pass with company_name, or pass application_id instead."

removedInput schema / required

Removed value: -[
-  "job_title",
-  "company_name"
-]

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "companyName": {
+      "type": "string"
+    },
+    "jobId": {
+      "type": "number"
+    },
+    "jobTitle": {
+      "type": "string"
+    },
+    "prepUrl": {
+      "type": "string"
+    },
+    "questionsToAsk": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "jobId",
+    "jobTitle",
+    "companyName",
+    "questionsToAsk",
+    "prepUrl"
+  ],
+  "type": "object"
+}

First observedv1.3.0Sep 7, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Beyond the annotations, the description discloses that a connected AI Applyd account is required, that the operation uses the user's AI credits, and that it saves the job to the account if not already saved. These are meaningful side effects and prerequisites that annotations alone do not convey.

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 compact and front-loaded with the deliverable, then covers usage, prerequisites, side effects, and exclusions. Every sentence earns its place, and the structure makes it easy for an agent to scan.

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

Completeness5/5

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

With a complete input schema and an output schema present, the description covers the remaining operational essentials: account requirement, credit consumption, job-saving side effect, and the routing rule. An agent has enough to select and invoke the tool correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the schema already documents each parameter. The description adds useful semantic context by explaining the mutually exclusive routing between application_id and the job_title/company_name pair, and clarifies that using application_id means no new saved job. This exceeds the baseline without adding much beyond the schema.

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 starts with an explicit verb and resource: 'Produce interview preparation for a specific role and company' and lists the concrete components it generates. It also distinguishes itself from aiapplyd_generate_cover_letter, making its purpose unmistakable.

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

Usage Guidelines5/5

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

It gives clear routing guidance: pass application_id for jobs the user applied to, or job_title and company_name otherwise. It also explicitly warns not to use it for cover letters and points to the correct sibling tool, which is strong when-to-use/alternative guidance.

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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