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update_live_shopify_product

Edit a LIVE Shopify product's title, description, or tags — changes buyers see immediately. Approval-tier with expected_updated_at lock: refuses if the product changed since the edit was reviewed. Use when the operator approves a change to live catalog.

Routing: Shopify: edit a LIVE product — approval-tier, lock-checked

[outbound-tier — EVERY call needs a manager's approval (per-send human rail): each request queues its own approval card and sends exactly once on approve. There is no standing grant for this tool.]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
titleNo
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
product_idYesProduct gid
description_htmlNo
expected_updated_atYesThe product's updatedAt as read when the edit was reviewed (ISO)

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden — and it does thoroughly. It reveals immediate buyer visibility, the approval lock via expected_updated_at, refusal on stale products, per-call approval requirement, single-send-on-approve behavior, and no standing grant. All are critical behavioral traits beyond the name.

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?

The description is well-structured and front-loaded with the primary meaningo, but it repeats the approval concept several times across the header, 'Approval-tier, lock-checked,' and the outbound-tier notice. Still organized, every sentence earns inclusion given the high-risk operation.

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?

A high-complexity edit tool with no annotations and no output schema receives enough practical detail for an agent to decide and invoke it. It covers when to use, what it does, the locking/approval workflow, and the refusal condition. Missing return format is a minor gap.

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 description has 50% coverage (, for name, product_id, companyId, expected_updated_at), and the description compensates by naming which fields are editable (title, description, tags, effectively mapping to title, description_html, tags). It also adds real-world meaning to expected_updated_at as an approval-lock semantic, though the schema already says 'read when the edit was reviewed.'

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 starts with a specific verb and resource: 'Edit a LIVE Shopify product's title, description, or tags.' This clearly differentiates it from draft-editing tools like update_shopify_ product"_draft by emphasizing 'LIVE' and the immediate buyer-visible effect.

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?

The description explicitly states when to use: 'Use when the operator approves a change to live catalog.' It does not explicitly name alternatives or state when-not scenarios, but the live-vs-draft contrast is strongly implied by the word 'LIVE' and sibling tool existence.

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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TDQS

A3.6/5.0
Disambiguation4/5

The tool set is heavily disambiguated by detailed routing descriptions, domain prefixes, and lifecycle verbs, so most tools have a clear intended purpose. However, at 297 tools there are still close pairs and overlapping decision surfaces (e.g., approval workflows, 'what should I work on' readers, multiple finance/ads readers) that require careful description reading to avoid misselection.

Naming Consistency4/5

Naming is predominantly consistent snake_case verb_noun with strong domain prefixes like shopify_, x_, posthog_, and list_/create_/update_ patterns. Minor inconsistencies exist, such as several collection-returning tools using get_ (get_team_members, get_icps, get_okrs) instead of list_, and some generate_ vs create_ vs draft_ verbs, but the pattern is still predictable overall.

Tool Count1/5

297 tools is an extreme outlier and far beyond a usable MCP tool surface. Even a large suite has no justification for this count in one server; the agent would struggle to select among hundreds of similarly descriptive tools, and the natural 3-15 tool range is exceeded by nearly 20x.

Completeness4/5

The individual domains represented — OKRs, CRM/leads, Shopify, content pipelines, ads, PostHog, team hiring, knowledge, finance, and session management — are covered remarkably well with full lifecycle patterns. Minor gaps exist, such as no full deal CRUD, no delete for several Google/Shopify artifacts, and some analytical surfaces being read-heavy, but most workflows can be completed without dead ends.

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