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Maestro Career Studio

Maestro Career Studio

Official
by seinun-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Get Autofill Profile

get_autofill_profile
Read-onlyIdempotent

Retrieves saved application-form autofill data—contact, work authorization, preferences, education, languages, custom answers, and resume employment/skills—for deterministic form filling.

Instructions

Read the user's application-form autofill data: profile (personal contact + address, work-authorization answers (work_auth: the job's country's, none without one; work_auth_by_country: per market key, e.g. UK; a question naming a country takes its), preferences like salary/notice/relocation, education, languages — each a language with read/speak/write level Basic/Intermediate/Fluent and separate native and fluent booleans — custom Q&A presets), plus employment blocks and skills from the selected resume when application_id or base is given. Response is labeled source: "profile" and includes canonical_identity (legal_first, legal_last, preferred) mapped only from stored autofill personal fields; it is the source of truth for form identity fields and takes precedence over ATS-parsed values. It is the feed the Chrome extension's deterministic fill uses. EEO/demographic answer values (profile.eeo) are returned ONLY when Profile standing consent is active (eeo_consent.enabled) and are stripped otherwise; when returned they are the user's stored answers, intended for Playwright/agent fill verbatim with no inference or model-authored values. eeo.veteran_status is no, yes or decline: one question, "Are you a veteran?" (a no also answers "not a protected veteran"; a yes alone never says which protected class applies, so leave a protected-class list to the user). eeo.gender is male, female, non_binary, self_describe or decline; with self_describe, eeo.gender_self_describe is the user's own words for a form's self-describe box. A form lacking a matching option is left to the user. Missing EEO values are completed by the user in Profile or in the browser; consented answers are not pasted or re-dictated through chat. A value absent here (a county) is not in the profile and is not guessable. WOTC, signatures and terms are human-only.

Input Schema

NameRequiredDescriptionDefault
baseNo
application_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.11.1

TDQS

A4.3/5.0
Behavior5/5

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

Despite annotations already covering readOnly and idempotent behavior, the description adds substantial behavioral detail: EEO values are returned only under active consent, otherwise stripped; returned EEO answers are verbatim stored answers with no inference allowed; missing values are not guessable; and canonical identity takes precedence over ATS-parsed values.

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

Conciseness3/5

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

The opening purpose is front-loaded, but the body is a dense run-on with nested parentheticals. Much of the EEO and consent detail is valuable, but the structure could be tightened or bulleted for easier parsing.

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 no output schema, the description carries the full burden of explaining returned data, and it does so thoroughly: response labeling, canonical identity precedence, conditional EEO consent behavior, and edge cases such as missing values are all covered.

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 0%, so the description must compensate for undocumented `base` and `application_id` parameters. It does explain that employment blocks and skills come from the selected resume when either parameter is given, but it does not define the difference between `base` and `application_id` or explain null/default behavior.

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 opens with a specific verb and resource: "Read the user's application-form autofill data." It then enumerates the profile contents and distinguishes this read operation from the sibling mutation tool `set_autofill_profile`.

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?

It gives clear usage context: this is the feed for the Chrome extension's deterministic fill and the source of truth for form identity fields. It also states important exclusions such as WOTC, signatures, and terms being human-only, though it does not explicitly compare when to use this versus other sibling read tools.

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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