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unpublish_shopify_product

Take a LIVE Shopify product off the storefront (status ACTIVE → DRAFT). Buyer-visible in reverse — removing a product buyers can currently see — so it is approval-tier and lock-checked with expected_updated_at. Use when the operator decides a live product comes down.

Routing: Shopify: take a live product DOWN — 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
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
product_idYesProduct gid
expected_updated_atYesThe product's updatedAt as read when the takedown was reviewed (ISO)

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of disclosure — and it delivers: per-send manager approval required, lock-checking via expected_updated_at, approval-card queuing, exactly-once send-on-approve, and no standing grant. This goes well beyond a naïive 'unpublishes a product' phrasing; the only notable omission is the return shape on success or lock failure.

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 purpose is front-loaded in the first sentence, followed by the when-to-use trigger, and then the routing/approval rail. There is slight redundancy ('approval-tier, lock-checked' appears twice), but every section (state change, visibility impact, approval constraints) earns its place — no filler words.

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 mutating, approval-gated tool with no output schema, the description explains the action, the storefront impact, the approval gate, the concurrency lock, and the once-only send semantics — enough for an agent to invoke it correctly and plan around the approval delay. The only residual gap is guidance on how to interpret the response when the approval card queues, which no schema exists to cover.

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 baseline is 3. The description adds genuine value by tying expected_updated_at to the lock-check mechanics ('lock-checked with expected_updated_at'), which explains why the timestamp matters beyond the schema's 'updatedAt as read when the takedown was reviewed'. The company scope and product gid meanings are already well covered in the schema.

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 states a precise action on a specific resource with an explicit state transition: 'Take a LIVE Shopify product off the storefront (status ACTIVE → DRAFT).' The buyer-visibility framing ('removing a product buyers can currently see') differentiates it from sibling tools such as publish_shopify_product, update_live_shopify_product, and update_shopify_product_draft without requiring the agent to open their schemas.

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 an explicit applicability trigger — 'Use when the operator decides a live product comes down' — and confirms an out routing line ('Shopify: take a live product DOWN'). It does not name the inverse alternative explicitly (e.g., publicate_shopify_product) or say 'do not use for editing', but the routing condition alone is unambiguous enough for an agent to select it correctly.

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.

Resources