GetMattered

Optimize Resume

getmattered_optimize_resume

Rewrite a resume to pass ATS screening, returning the rewritten resume, projected ATS score, and itemized changes; add a job description to tailor it to a specific role.

Instructions

Rewrite a resume so it passes ATS screening. Returns the rewritten resume, a projected ATS score, and an itemised summary of every change made. Pass job_description to tailor the rewrite to one specific posting, or omit it for general ATS optimization. The rewrite is returned in the answer; the resume on the account does not change unless the user saves it with getmattered_set_resume. Requires a connected GetMattered account. Uses the user's AI credits. Do not use it to apply: getmattered_apply tailors the resume for every application on its own. resume_text is optional: the user can paste or attach a resume in the chat, and you pass its text as resume_text. Without it, the tool uses the resume already on the user's account. In ChatGPT the user can attach a resume file; pass it as resume_file.

Input Schema

Name	Required	Description
`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	Full text of the resume to optimize. The resume the user pasted or attached in this conversation, as plain text. Omit it to use the resume already on the user's GetMattered account.
`job_description`	No	Full text of the target job description (optional, omit for general ATS optimization)

Output Schema

Name	Required	Description	Default
`changes`	Yes
`optimizedResume`	Yes
`projectedAtsScore`	Yes

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?

Adds real behavioral context beyond the annotations: the rewrite is returned inline and does not mutate the account resume unless saved via getmattered_set_resume, and the call consumes AI credits and requires an authenticated account. That resolves the ambiguity left by readOnlyHint=false on an operation that persists nothing.

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?

Key facts (rewrite, returns, tailoring branch) are front-loaded and each sentence carries information, but the resume_file sentence is duplicated nearly verbatim in the schema description, and the closing lines repeat the account/cost constraints already implied earlier.

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?

An output schema exists so return values need no explanation, and the description nonetheless covers the three-way input decision (resume_text / resume_file / account default), prerequisites, cost, and the sibling that should be used instead for applying. Nothing an agent needs to invoke it correctly is missing.

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 carries the field definitions; the description still adds the fallback logic — omitting resume_text causes the tool to use the resume already on the account, and resume_file is the ChatGPT attachment path. Slight redundancy with the schema text keeps it from 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?

Specific verb+resource ('Rewrite a resume so it passes ATS screening') plus explicit outputs, and it distinguishes itself from siblings by naming getmattered_apply and getmattered_set_resume as the alternatives for applying and saving.

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 branching conditions: pass job_description to tailor to one posting, omit it for general optimization; do not use it to apply because getmattered_apply handles that. It also states prerequisites (connected account) and cost (AI credits).

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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