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set_shopify_variant_price_draft

Set a variant's price (and optionally compare-at price) on a DRAFT Shopify product. Refuses variants of live (ACTIVE) products — repricing what buyers see needs the approval-gated live tool. Use when a person or agent is pricing unpublished catalog.

Routing: Shopify: set price on a DRAFT product variant — refuses live products

[sensitive-tier — first use may require a manager's approval; a from-now-on approval makes future calls seamless, a just-once approval re-asks next time.]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
priceYesDecimal price in the shop currency, e.g. "19.99"
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
product_idYesParent product gid
variant_idYesVariant gid (gid://shopify/ProductVariant/...)
compare_at_priceNoOptional compare-at (strikethrough) price

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 behavioral disclosure. It covers refusal behavior for live products, the sensitive-tier approval requirement, and the approval semantics (from-now-on vs just-once). It doesn't describe success responses or exact side effects, but the core behavior is well disclosed.

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 mostly concise but contains some redundancy: the opening sentence and Routing line both state 'set price on a DRAFT product variant — refuses live products'. The sensitive-tier note is important and earns its place. Slight repetition prevents a perfect score.

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?

Given 5 parameters, no output schema, and no annotations, the description effectively covers purpose, scope (draft only), exclusions (live products), approval implications, and a clear use case. It doesn't explain return values, but for a simple pricing update tool this is not a critical omission.

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 the schema already documents all parameters. The description adds only that compare-at price is optional, which is already implied by the schema. Baseline 3 applies because the description adds minimal value beyond the structured parameters.

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 clearly states the tool sets a variant's price (and optionally compare-at price) on DRAFT Shopify products, using a specific verb and resource. It explicitly distinguishes itself from the live-product tool by refusing live products, making its purpose unambiguous.

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

Usage Guidelines5/5

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

The description explicitly instructs when to use it ('Use when a person or agent is pricing unpublished catalog') and when not to ('Refuses variants of live (ACTIVE) products'), even naming the alternative ('repricing what buyers see needs the approval-gated live tool'). This provides excellent guidance for tool selection.

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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