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publish_shopify_page

Publish an UNPUBLISHED Shopify page live to buyers. Approval-tier with expected_updated_at lock (refuses if the page changed since review). Use when the operator green-lights drafted site content going live. (Dogfood flag: page updatedAt field shape verified on first live connect.)

Routing: Shopify: publish a drafted page LIVE — 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
page_idYesPage gid
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
expected_updated_atYesThe page's updatedAt as read when reviewed (ISO)

TDQS

A4.5/5.0
Behavior5/5

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

No annotations are provided, so the description carries full behavior burden. It goes well by disclosing: approval as a side effect, the lock up normal, refusal if changed, absence standing grant, and that a request queues its own approval and sends once. This is strong behavioral transparency for a mutating tool.

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 allowative first sentence is front-loaded, but the Routing section repeats the same publish action and the Dogfood flag is an implementation note. It is not concise or repetitive but still organized.

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?

Given there no annotation not output schema, the description provides what an agent needs to call the tool with due care: exact target object, lock precondition, operator go-ahead, and proceed way. No essential call-site information appears 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% and normally baseline descri, ehich gives expected update time lock semantics beyond what the schema's raw wording states: it refuses if the page changed since review. That is usable and adds meaning.

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 first sentence explicitly identifies the operation: publishing an UNPUBLISHED Shopify page live to buyers. It clearly names the object, the verb, and the destination state, and it distinguishes the tool from sibling product-publishing or draft-editing tools.

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 states a concrete usage condition: use it when an operator green-lights drafted site content going live. It communicates the approval requirement, but it does not explicitly compare against alternatives such as update_shopify_page_draft or publish products, so the boundary is mostly implicit.

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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