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Localize chosen listing

optimizer_localize

Creates per-locale listing drafts by translating core phrases and measuring local phrasings in each locale's test market, then using the best. Failed locales do not block others.

Instructions

Write per-locale drafts of the chosen listing: the core phrases are translated, candidate local phrasings are measured in each locale's test market, and the best are used. A locale that fails does not stop the others; get_optimizer lists failedLocales and lastError. Background job.

Input Schema

NameRequiredDescriptionDefault
scopeNoWithout locales: the tracked markets' locales (markets, default) or every listing locale of the store in fast mode (all: batched AI candidates, parallel, measured in each locale's test market only).
localesNoOnly these locales (default: see scope).
projectIdYesProject id (from get_overview).
remainingNoOnly locales without a draft yet or that failed last time (finish an interrupted run).
directionsNoOptional creative direction for the AI, e.g. "emphasise offline use".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.7

TDQS

A3.7/5.0
Behavior4/5

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

Adds real behavior beyond the annotations: it is a background (async) job, translation plus market measurement is performed, and one failed locale does not abort the rest, with failedLocales/lastError exposed via get_optimizer. This is meaningful context for a non-read-only, non-idempotent, open-world operation. It stops short of saying whether existing drafts are overwritten or how to poll for completion.

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?

Three short sentences, purpose first, failure semantics second, async nature last. Dense in the good sense, with only minor redundancy in describing the candidate-measurement pipeline rather than just the effect.

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

Completeness4/5

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

For a 5-parameter background mutation with no output schema, the description covers the effect, partial-failure behavior, and where to check status. It could add the completion/polling path (wait_for_jobs) and whether re-running replaces drafts, but nothing critical to invoking it correctly is missing.

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 100%, so scope, locales, remaining and directions are already well documented in the schema. The description echoes the locale/test-market concept but adds no syntax or defaulting detail for scope, locales, or remaining beyond what the schema states. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

"Write per-locale drafts of the chosen listing" gives a specific verb and resource, and the follow-on sentence explains what the write actually does (translate, measure candidates in the locale's test market, keep the best). It reads clearly as a distinct step from siblings like optimizer_choose or optimizer_edit_locale, though it never names those alternatives to draw the boundary explicitly.

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

Usage Guidelines3/5

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

"The chosen listing" implies this runs after a listing has been selected, and the reference to get_optimizer's failedLocales gives an implicit recovery path. But there is no explicit when-to-use/when-not-to-use guidance, no pointer that this is a background job requiring wait_for_jobs, and no statement of the prerequisite choice step.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.