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list_shopify_discounts

List discount codes and automatic discounts configured on the connected Shopify store — id, discount type, title, and status (ACTIVE/EXPIRED/SCHEDULED). Use to see what promotions currently exist before creating or referencing one.

Routing: Shopify discounts: title/type/status for codes and automatic discounts

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many to return (default 20, max 50)
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so description carries burden. It mentions data returned (id, type, title, status) but does not disclose pagination behavior beyond limit param, sorting, or whether it returns both codes and automatic discounts in one call. It implies read-only but doesn't state side effects (likely none). Adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first lists what it returns and purpose, second provides routing hint. Zero filler, front-loaded with key info. The routing line is brief but useful.

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?

Tool is simple with 2 params and full schema coverage, no output schema needed. Description covers purpose, return fields, and usage context. Slight gap: doesn't mention if discounts are returned in any order or how statuses are counted, but sufficient for agent selection.

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 coverage is 100% and parameters are simple: companyId (required) and limit with default/max. Description adds 'company-scoped' context for companyId and mentions statuses, but does not explain how limit interacts or whether discount type filtering is possible. Baseline 3 is appropriate as schema covers semantics well.

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?

Description clearly states it lists discount codes and automatic discounts from Shopify, including id, type, title, and status. It adds scope (code vs automatic) and clarifies use case before creating or referencing promotions. Distinguishes from create_shopify_discount_code and other list_shopify_* 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?

Explicitly says 'Use to see what promotions currently exist before creating or referencing one', which provides clear context. However, it does not explicitly mention when NOT to use or name alternative tools for filtering or more detailed discount info. Sibling names like create_shopify_discount_code imply creation, but no direct comparison is made.

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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