AI Applyd

Set Resume

aiapplyd_set_resume

Set your base resume for tailored job applications, finish account setup, and trigger AI Applyd to find matching jobs. Pass resume text, an https file URL, or a saved document ID.

Instructions

Put the user's resume on their AI Applyd account as the base every application is tailored from. Pass exactly one: resume_text (the full text), resume_url (an https link to a PDF, DOCX or plain text file, up to 10 MB), or document_id (one of their saved resumes, from aiapplyd_get_account). A new upload is read by AI and uses the user's AI credits; switching to a saved resume with document_id is free. It also finishes account setup, so AI Applyd starts finding jobs for them. Use it when aiapplyd_get_account shows no resume on file, or when the user gives a new one. Do not use it to rewrite or score a resume; use aiapplyd_optimize_resume or aiapplyd_score_resume. Next: aiapplyd_get_matches.

Input Schema

TableJSON Schema

Name	Required	Description
`resume_url`	No	An https link to the resume file: PDF, DOCX or plain text, up to 10 MB. Pass this OR resume_text OR document_id.
`document_id`	No	One of the user's saved resumes to make the base (an id from aiapplyd_get_account). Free. Pass this OR resume_text OR resume_url.
`resume_text`	No	The full text of the resume. Pass this OR resume_url OR document_id.

Output Schema

TableJSON Schema

Name	Required	Description
`parsed`	Yes	False when the resume is still being read; call aiapplyd_get_account in a minute.
`source`	Yes
`fullName`	Yes
`documentId`	Yes	The resume now used as the base for every application.
`skillsCount`	Yes
`currentJobTitle`	Yes
`preferencesSeeded`	Yes	True when empty job preferences were filled from this resume.
`suggestedJobTitles`	Yes
`onboardingCompleted`	Yes	True when the account setup is finished, so AI Applyd searches for this user.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.8.0Sep 25, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations only indicate non-read-only, non-idempotent, and non-destructive behavior. The description adds meaningful behavioral context: a new upload consumes the user's AI credits, switching to a saved resume with document_id is free, and setting the resume also completes account setup and triggers job finding. This is valuable side-effect information beyond what the annotations provide.

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 relatively long, but every sentence earns its place: purpose, parameter selection, side effects, use conditions, exclusions, and next step. It is front-loaded with the core purpose and structured logically, so an agent can quickly extract the essential behavior without wading through filler.

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?

Given the tool's complexity and the presence of annotations and an output schema, the description covers everything an agent needs: what the tool does, how to choose a parameter, credit cost, account setup implications, when to use it, when not to use it, and the recommended next tool. There are no meaningful gaps.

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%, but the description still adds semantic value by clarifying that exactly one of the three parameters must be passed and by explaining the cost implication: uploading a new resume uses AI credits, while using document_id is free. This is genuinely useful guidance beyond the schema's field descriptions.

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 and resource: it puts the user's resume on their AI Applyd account as the base for tailoring applications. It also distinguishes itself from sibling tools by explicitly saying it should not be used to rewrite or score a resume, naming aiapplyd_optimize_resume and aiapplyd_score_resume as the correct alternatives.

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?

The description gives explicit conditions for use: when aiapplyd_get_account shows no resume on file, or when the user provides a new resume. It also states what not to use it for and directs the agent to specific sibling tools, making the selection decision unambiguous.

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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