Building a Shopify MCP Server: AI Agents Meet E-Commerce

The Model Context Protocol (MCP) is one of those ideas that feels obvious in retrospect. LLMs are good at reasoning and generating text. They're bad at doing things — managing inventory, updating prices, creating products. MCP bridges that gap by giving AI models a standardized way to call external tools.

I built a Shopify MCP Server that lets any MCP-compatible AI agent manage a Shopify store through natural language. It's now listed on 5+ MCP registries including Glama, Smithery.ai, and MCP.so, and featured in the awesome-mcp-servers list.

Here's how I built it and what I learned about designing tools for AI agents.

Why Shopify + MCP

E-commerce is one of the best use cases for AI agents because the operations are structured, repetitive, and high-volume. A store owner might need to:

• Update prices across 500 products based on new supplier costs
• Create product listings from a spreadsheet
• Check inventory levels and flag items below threshold
• Generate product descriptions from specifications

These are all tasks where a human opens the Shopify admin, clicks through menus, and does manual data entry. An AI agent with MCP tools can do the same thing through natural language: "Set all summer collection items to 20% off" or "Create a new product from this spec sheet."

Shopify's Admin API is GraphQL-based, which is actually ideal for MCP. GraphQL lets you request exactly the fields you need, which means less token waste when the LLM processes the response.

Architecture

The server is structured around three layers:

1. MCP Tool Definitions

Each Shopify operation is exposed as an MCP tool with a clear name, description, and input schema. The tool descriptions are critical — they're what the LLM reads to decide which tool to use and how to call it. I spent more time writing tool descriptions than writing the actual GraphQL queries.

For example, the product creation tool doesn't just say "creates a product." It specifies: what fields are required, what formats are expected, what happens on success, and what errors look like. The better the tool description, the fewer hallucinated parameters the LLM produces.

2. GraphQL Query Layer

Each tool maps to one or more Shopify Admin API GraphQL mutations or queries. I use parameterized queries with proper input validation before hitting the API. This layer handles:

• Query construction with proper variable types
• Input sanitization (prices as strings, not floats — Shopify's requirement)
• Pagination for bulk operations
• Error mapping from GraphQL errors to human-readable messages

3. Response Formatting

The raw GraphQL response gets transformed into something the LLM can reason about. This means stripping unnecessary metadata, flattening nested objects where sensible, and including context the LLM needs for follow-up actions (like the product ID after creation, so it can immediately add variants).

Tool Design Lessons for MCP

Building tools for AI agents is different from building APIs for humans. Here's what I learned:

Name your tools like you're naming CLI commands

LLMs pick tools based on the name and description. shopify_create_product is better than createResource. Be specific, use underscores, and include the domain context.

Descriptions are your documentation

The LLM has no README. The tool description IS the documentation. If you wouldn't feel comfortable explaining the tool to a junior developer in 2-3 sentences, your description needs work.

Return actionable data

When a tool creates something, return the ID and key fields in the response. The LLM will likely need them for the next step. Don't make it call a separate "get" tool just to find the thing it just created.

Fail loudly and specifically

Generic error messages like "operation failed" are useless to an LLM. Return the specific field that failed validation, the expected format, and ideally a suggestion for fixing it. The LLM will use that context to retry intelligently.

Keep tool count manageable

It's tempting to create a tool for every possible Shopify operation. Don't. Start with the 10-15 operations that cover 80% of use cases. Too many tools confuse the LLM's selection process. You can always add more based on actual usage patterns.

Getting Listed on MCP Registries

The MCP ecosystem is still early, which means getting visibility is about being present in the right places. I submitted the server to:

• awesome-mcp-servers — the community-curated GitHub list
• Glama — MCP server discovery platform
• Smithery.ai — MCP marketplace
• MCP.so — MCP server directory
• PulseMCP — another growing registry

The submission process varies. Some accept pull requests, others have forms. The key is having clean documentation, a working demo, and clear instructions for getting the server running. Most registries review submissions manually, so quality matters.

What's Next

The MCP ecosystem is growing fast. I'm seeing more LLM clients add native MCP support, which means tools like this become immediately useful to a larger audience. The Shopify MCP Server is one of the early production-ready implementations, and I plan to expand it with:

• Bulk operation support for large catalogs
• Order management tools
• Analytics and reporting queries
• Webhook registration for real-time event handling

If you're building MCP servers, the opportunity right now is in connecting AI agents to the tools people already use. Shopify, Stripe, GitHub, Notion — these are all platforms where structured operations can be exposed as MCP tools with relatively low effort and high impact.

Check out the project on GitHub.