Skip to main content
Glama
Sign In

Shopify MCP by InsightfulPipe

by com.insightfulpipe
OverviewDiscussionsAdmin

Server Details

Hosted Shopify MCP server for Claude, ChatGPT and Cursor, with each action switched per account.

Ownership verified
Status
Healthy
Uptime
100.0% over the last 24 hours
OAuth
Works in Glama
Last Tested
Transport
Streamable HTTP · MCP 2025-11-25
URL
Repository
Insightful-Pipe/shopify-mcp-server
GitHub Stars
1
ChatGPT
–
Claude
–

TDQS

A3.9/5.0

Scored across 4 tools

Disambiguation4/5

The four tools have mostly distinct roles: query_contexts for metadata/planning, query_data for reading Shopify data, execute_action for performing actions, and prompts_menu for prompt templates. The main ambiguity is between query_contexts and query_data (both 'query' tools) and the fact that query_data and execute_action are both labeled as 'Step 3 (FINAL)', requiring the agent to choose between read vs. write paths. Descriptions clarify the intended sequence, so confusion is limited.

Naming Consistency4/5

Three tools follow a clear verb_noun pattern (execute_action, query_contexts, query_data), all in snake_case. The outlier is prompts_menu, which is a noun_noun construction rather than a verb-led name. The deviation is minor and understandable, but prevents a perfect score.

Tool Count4/5

With only four tools, the server uses a generic dispatcher pattern to cover a broad Shopify surface, which is efficient and well-scoped. Each tool has a clear role in the workflow (context discovery, data querying, action execution, prompt browsing). Slightly under the ideal range, but appropriate for a meta-tool design.

Completeness4/5

The generic query_data and execute_action tools, backed by query_contexts' available_actions and actions_details, can theoretically cover the full Shopify lifecycle without needing individual CRUD tools. Minor gaps exist: there is no direct schema/error-handling tool, and completeness depends entirely on the backend's available_actions list. Overall, the surface is broad and avoids dead ends for the stated purpose.

Available Tools

4 tools
execute_actionShopify Execute ActionA
Destructive
Inspect

Execute Shopify action (Step 3 (FINAL) of workflow).

REQUIRED: Complete these steps first using query_contexts:

  1. request='accounts' - Get workspace_id, brand_id AND available_actions

  2. request='actions_details' with actions=['xxxxxx', 'xxxxxx', 'xxxxxx'] - Get detailed body structure for specific action(s) using actions from available_actions

IMPORTANT: Always call step 2 to see the exact body structure before executing.

Rely on info from steps 1-2 to construct the action body. Follow the schema exactly.

Inputs: body (required). Returns: object

prompts_menuShopify PromptsA
Read-only
Inspect

Browse the Shopify prompt catalog.

  1. request='menu' – List public/system prompts (optionally pass workspace_id + prompt_type).

  2. request='prompt_details' – Fetch prompt body by prompt_id. Set is_custom=True + workspace_id for workspace prompts.

Inputs: request (required), is_custom, prompt_id, prompt_type, +1 more. Returns: object

query_contextsShopify Setup HelperA
Read-only
Inspect

Helper for planning InsightfulPipe Shopify data queries.

Step 1: request='accounts' – Retrieve workspace_id, brand_id, other source metadata, AND available_actions (all tables you can query).

Step 2: request='actions_details' with actions=['xxxxxx', 'xxxxxx', 'xxxxxx'] – REQUIRED before querying. Replace with actions from available_actions to get the exact request body, required identifiers, fields, time ranges, and notes for each action.

Optional: request='available_actions' – Standalone call to list all available tables/actions (already included in accounts response).

Optional: request='brands' with brand_id + workspace_id to pull brand guidelines.

Inputs: request (required), actions, brand_id, platform, +1 more. Returns: object

query_dataShopify Data QueryA
Read-only
Inspect

Query Shopify data (Step 3 (FINAL) of workflow).

REQUIRED: Complete these steps first using query_contexts:

  1. request='accounts' - Get workspace_id, brand_id AND available_actions

  2. request='actions_details' with actions=['xxxxxx', 'xxxxxx', 'xxxxxx'] - Get detailed body structure for specific action(s) using actions from available_actions

IMPORTANT: Always call step 2 to see the exact body structure before querying.

Rely on info from steps 1-2 to construct the query body. Follow the schema exactly.

Inputs: body (required). Returns: object

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Related MCP Connectors

    Related MCP Servers

    • Clind MCP Server

      lirad
      F
      license
      Not graded
      quality
      Not graded
      maintenance
      A Shopify-focused MCP server that enables AI agents to manage store operations like order tracking, product discovery, and checkout link generation. It facilitates customer-facing interactions including shipping estimates and real-time inventory searches.
      -

    • Shopify MCP Server

      sahididrishi
      A
      license
      Not graded
      quality
      D
      maintenance
      MCP server that connects Claude to Shopify stores, enabling natural language queries and actions on products, orders, customers, inventory, and sales analytics. Includes a demo mode with bundled fixtures for trying tools without credentials.
      116 npm
      MIT

    • mcp-shopify

      cloud9-labs
      A
      license
      C
      quality
      D
      maintenance
      MCP server for Shopify Admin API. Enables product, order, customer, and inventory management via natural language.
      14
      8 npm
      1
      MIT
    Try in Browser
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    Glama MCP Gateway

    Add one secure layer between your agents and this server.

    Resources