GetMattered

Set Resume

getmattered_set_resume

Destructive

Set a base resume on a GetMattered account from text, an HTTPS URL, a saved resume, or an attached file so job applications are tailored from it and job matching can start.

Instructions

Put the user's resume on their GetMattered 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 getmattered_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 GetMattered starts finding jobs for them. Use it when getmattered_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 getmattered_optimize_resume or getmattered_score_resume. Next: getmattered_get_matches. In ChatGPT the user can attach a resume file; pass it as resume_file. It replaces the user's current base resume (the old one stays in their documents), so call it only when the user wants this resume used.

Input 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 getmattered_get_account). Free. Pass this OR resume_text OR resume_url.
`resume_file`	No	In ChatGPT the user can attach a resume file; pass it as resume_file. It is downloaded once, right away: PDF, DOCX or plain text, up to 10 MB.
`resume_text`	No	The full text of the resume. Pass this OR resume_url OR document_id.

Output Schema

Name	Required	Description
`parsed`	Yes	False when the resume is still being read; call getmattered_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 GetMattered searches for this user.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.9.1Oct 25, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already flag destructiveHint=true and non-readOnly, but the description adds substantively: a new upload is read by AI and consumes AI credits, document_id switching is free, and it replaces the current base resume while the old one is retained in documents. The credit-cost distinction and replacement semantics are exactly the behavioral context annotations cannot convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose and the selection constraint, then usage conditions, next step, and a replacement caveat. Dense but nearly every sentence carries operational value; the closing 'call it only when the user wants this resume used' mildly restates the earlier replacement point.

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?

For a destructive mutation with a nested object parameter and an output schema, the description covers prerequisites, mutually exclusive inputs, cost implications, side effects, and the follow-up call. An agent has everything needed to invoke it 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 baseline is 3. The description goes beyond it by enforcing 'pass exactly one' across the four inputs and explaining the cost tradeoff between a fresh upload (AI credits) and document_id (free), plus the ChatGPT resume_file path. It does not add format details beyond the schema, so not a 5.

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?

States a specific verb and resource ('Put the user's resume on their GetMattered account as the base every application is tailored from') and explicitly names the siblings it is not (getmattered_optimize_resume, getmattered_score_resume). An agent can distinguish it from the resume-related siblings without opening any schema.

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?

Gives explicit triggers ('when getmattered_get_account shows no resume on file, or when the user gives a new one'), explicit exclusions ('Do not use it to rewrite or score a resume'), named alternatives for those cases, and a next-step handoff (getmattered_get_matches). This is about as complete as usage guidance gets.

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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