Get the connected Shopify store's profile: name, primary domain, currency, plan, and contact email. Use to confirm which store the agents are connected to.
Routing: Shopify store identity (name/domain/currency/plan) from the live Admin API
| Name | Required | Description | Default |
|---|---|---|---|
| companyId | Yes | FreedomOS company id to act within (you must be a member). Required for company-scoped tools. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. The description indicates it fetches live data from the Admin API ('from the live Admin API'), which is useful, but it does not disclose potential side effects, authentication requirements, or that it might require specific permissions. It's a read operation, but without annotations the description could have more explicitly stated this.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, with two sentences: the first provides the primary purpose and fields, the second gives routing context. No redundant information is included.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema, low complexity), the description is fairly complete. It covers what the tool does, why it is used, and the data source, but does not detail the output format or potential errors, which may be acceptable for a simple read operation. Slight deduction for not mentioning that it only returns one record.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one parameter (companyId), which is fully described as being required for company-scoped tools. The description does not add any additional parameter semantics beyond what the schema already provides. With high schema coverage, a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves the connected Shopify store's profile and lists the specific fields (name, domain, currency, plan, contact email). It also specifies the use case of confirming which store agents are connected to, distinguishing it from other Shopify tools that operate on products or orders.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear context for when to use it (confirming store identity), but it does not explicitly state when not to use it or which alternatives exist. Among siblings, there are other get_shopify_* tools (e.g., get_shopify_product, get_shopify_order) that serve different purposes, but the description does not contrast with them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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 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.
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.
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.