@apparelhub/mcp-server

@apparelhub/mcp-server

Workflow-level MCP tools that let an AI agent run an ApparelHub store end to end: set it up, design apparel, build products, list them across sales channels, and work the orders and listings that come back. Each tool wraps the ApparelHub Agent API and bakes in the platform's hard-won production lessons, so the agent gets correct behavior for free instead of learning the gotchas itself.

There are two ways to run it: a hosted connector you authorize with OAuth (recommended, no API key to handle) and this npm package you run yourself with a key. Both serve the identical tool surface.

Status: early access. The npm package is pre-1.0 while the surface stabilizes. The agent-facing tool surface is v1 and is the contract we keep stable (see CHANGELOG.md and Versioning).

What makes this different

A thin wrapper around a REST API just renames HTTP calls. These tools are at the workflow level: one ship_product call resolves variants, generates and waits for a mockup (through the waiting until its images are actually published), creates the product with the right field names, adds every variant, associates it with a store, and syncs to fulfillment and channels in the correct order, refusing a negative-margin price and warning on known variant traps along the way. The scar tissue lives in the code, not in your agent's context.

Connect

Prefer the hosted connector. Authorizing it provisions the API key for you, so there is no credential to create, paste, rotate, or leak into a config file. Reach for the self-run package only when your client cannot speak remote MCP or OAuth, or when you specifically want the server running on your own machine.

	Hosted connector (OAuth)	Self-run package (API key)
Credential	Provisioned for you on approval	You create and paste an API key
Local prerequisites	None	Node.js 20+, Python 3 + Pillow, optionally tesseract
Transport	Remote MCP over HTTP	stdio
Best for	Any client that supports remote MCP	Clients without remote/OAuth support, local control, development

Recommended: the hosted connector

https://mcp.apparelhub.ai

Add that URL to any MCP client that supports remote servers, sign in to ApparelHub, and approve. You never handle an API key. Approving the grant provisions a connector key on your account (or reuses the one you already have), and the hosted server resolves your session to it on every call. Nothing to paste, nothing to rotate by hand, nothing sitting in a dotfile.

The hosted server also carries the imaging toolchain the design and quality tools need, so transparency keying, image statistics, and OCR all work with no local dependencies at all: no Node, no Python, no Pillow, no tesseract.

Claude Code

claude mcp add --transport http apparelhub https://mcp.apparelhub.ai

Then run /mcp and choose Authenticate.

Any client that reads an MCP config file

{
  "mcpServers": {
    "apparelhub": {
      "type": "http",
      "url": "https://mcp.apparelhub.ai"
    }
  }
}

claude.ai — add it as a custom connector under Settings → Connectors, paste the same URL, then authorize when prompted.

The bare origin above is the server's canonical address: it is the resource identifier its own OAuth metadata advertises. A path is not routed on, so an existing config pointing at https://mcp.apparelhub.ai/mcp keeps working and needs no change.

What the handshake actually does

Standard OAuth 2.1, nothing custom for you to configure. Your client discovers the authorization server from /.well-known/oauth-protected-resource (RFC 9728), registers itself dynamically, and runs an authorization-code flow with PKCE (S256) for the mcp scope against https://api.apparelhub.ai. Refresh tokens and revocation are supported. Access tokens are opaque: the hosted server exchanges yours for your connector key server-side, so the key is never in your client, your config, or your chat history.

Key slots and revoking

One connector key is minted per account and shared across every chat surface you grant, and it counts against your plan's API key allowance. The Free plan includes API access with one slot, so if that slot is already taken by a self-service key the consent screen says so and offers to free it or upgrade. To revoke, disconnect from the client that holds the grant, or delete the connector key at https://apparelhub.ai/developer/api-keys.

Alternative: run the package yourself

Requirements:

• Node.js 20+.
• An ApparelHub account and API key — generate one at https://apparelhub.ai/developer/api-keys.
• For the design + quality tools only: Python 3 with Pillow (transparency keying, image QC) and optionally tesseract (OCR text detection). These run locally; if they're missing, those tools return a clear notice telling you exactly what to install, and never crash.

The server reads your key from the APPARELHUB_API_KEY environment variable at startup and speaks MCP over stdio. It never accepts the key as a tool argument, and the API host is pinned (no override).

Claude Code

// ~/.claude/mcp.json (or a project .mcp.json)
{
  "mcpServers": {
    "apparelhub": {
      "command": "npx",
      "args": ["-y", "@apparelhub/mcp-server"],
      "env": { "APPARELHUB_API_KEY": "your-key-here" }
    }
  }
}

Cursor

// .cursor/mcp.json
{
  "mcpServers": {
    "apparelhub": {
      "command": "npx",
      "args": ["-y", "@apparelhub/mcp-server"],
      "env": { "APPARELHUB_API_KEY": "your-key-here" }
    }
  }
}

Aider

# .aider.conf.yml
mcp-servers:
  apparelhub:
    command: npx
    args: ["-y", "@apparelhub/mcp-server"]
    env:
      APPARELHUB_API_KEY: your-key-here

Any other MCP client

Same shape everywhere: run npx -y @apparelhub/mcp-server with APPARELHUB_API_KEY in its environment.

Environment variables

Variable	Purpose
APPARELHUB_API_KEY	Required. Your ApparelHub API key. Not needed on the hosted connector.
APPARELHUB_MCP_TELEMETRY	Set to off to disable the coarse usage signal (see Privacy).
APPARELHUB_MCP_PYTHON	Path to the Python 3 interpreter for the local image tools (default python3).

Tools

121 tools. docs/TOOLS.md walks through the core groups; call tools/list from your agent for the authoritative live schemas.

• Setup & connect — check_setup_readiness (what the account has, what it needs, the single next action), list_connectable_providers, connect_fulfillment_provider and connect_sales_channel (API-token providers connected entirely in chat), plus start_channel_connect / check_connection_status for the browser-based ones (Printful, Shopify, TikTok Shop, Fourthwall).
• Read — list_my_workspaces, list_my_stores, list_my_designs, list_my_products, list_my_orders, get_order_details.
• Catalog — browse_catalog, get_garment_details, find_garments (search every connected provider at once for a capability), recommend_garment, list_catalog_providers.
• Design — design_apparel, iterate_design, upload_design (bring artwork the merchant already owns), fit_aspect (quota-free reshape), design lifecycle (archive_design, restore_design, delete_design), and split primitives generate_image, process_transparency, verify_design_text.
• Product — ship_product, update_product, delete_product, unsync_from_channel, diagnose_tiktok_listings, and split primitives create_product, add_variants, sync_to_fulfillment, sync_to_channel.
• Orders — lifecycle (approve_order, unapprove_order, hold_order, cancel_order, confirm_order, submit_order_to_fulfillment, check_order_status, reconcile_order), draft edits (add_order_item, remove_order_item), and design-approval holds (list_order_holds, approve_order_hold, request_hold_changes).
• Fulfillment issues — report_fulfillment_issue (report a defect on an order), list_fulfillment_issues (per-order or workspace-wide inbox), check_fulfillment_issue (full issue plus the provider-ready problem report), resolve_fulfillment_issue (record the provider filing, close with a resolution, or create a replacement order).
• Channel intelligence — what the sales channel itself reports and what you can set on it: channel_performance, channel_opportunities, channel_coverage, listing_changes (did the last edit actually work), describe_listing_attributes, set_listing_attributes, set_channel_settings, import_size_measurements.
• Analytics — analytics_summary, analytics_timeseries, analytics_breakdown, analytics_ops, analytics_portfolio.
• Collections — list_collections, get_collection, create_collection, update_collection, delete_collection, add_products_to_collection, remove_product_from_collection, sync_collection.
• Cross-workspace transfer — copy_product_to_workspace, move_product_to_workspace, check_product_move, and the design equivalents.
• Workspace & team management (agency / Enterprise, account-wide key) — workspaces (create_workspace, update_workspace, delete_workspace, check_workspace_deletion, assign_workspace_member, unassign_workspace_member, move_store_to_workspace, get_role_matrix) and team (get_account_overview, list_account_members, remove_member, invite_member, list_invites, revoke_invite, resend_invite, accept_invite).
• Store & order management — store settings/lifecycle (get_store_settings, update_store_settings, create_store, archive_store, unarchive_store, activate_store), order payment/ops (record_order_payment, mark_order_no_payment, set_order_payment_method, sync_orders, estimate_order_costs, get_orders_summary, list_pending_fulfillments), and archive_product / restore_product.
• Systems of action — analyze_what_works, auto_optimize_listings, cascade_price_change, set_prices_by_margin, recover_from_outage.
• Safety — verify_design_quality, verify_mockup_quality, check_design_compliance.
• API escape hatch — get_api_reference (discover the full agent API from the live OpenAPI spec) and api_request (call any /agents/v1 endpoint when no dedicated tool fits).

Read tools are read-only. Product and design tools default to draft, never live, enforce pricing floors, and guard known variant traps. Systems-of-action mutations default to a dry run and only take safe actions (archive, never delete) when applied. Every product/order/store result carries a view_url back into apparelhub.ai. Errors come back in a consistent shape ({error: {code, message, retry_after?, suggestion?}}) — tools never throw across the MCP boundary.

get_api_reference also reports what the server you are actually talking to serves — its version, tool count, and every tool name — so an agent can tell "that tool does not exist" apart from "my cached tool list is stale" instead of guessing.

Privacy

An optional, coarse usage signal helps improve the tools. It sends only non-identifying features — the tool name, outcome, latency, error code, and a strict allowlist of coarse fields (e.g. AI source name, garment category). It never sends prompts, images, ids, URLs, or customer data. It's buffered and fire-and-forget (it can never affect a tool call). Turn it off entirely with APPARELHUB_MCP_TELEMETRY=off.

The hosted connector additionally records one operational metric per request (outcome, latency, and for a tool call the tool name). It carries no per-identity dimension and no user data.

Skill vs. MCP vs. recipes

ApparelHub ships the agent surface in three forms:

• The markdown skill is the lowest-friction way to use ApparelHub from Claude Code — it teaches the agent the REST API and the design rules directly.
• This MCP server turns that knowledge into a typed, callable tool surface (with the systems-of-action tools) that works across any MCP-capable agent, and, through the hosted connector, in chat surfaces that cannot run a skill or a local process at all.
• The recipes are end-to-end operating blueprints you drop into your agent's runtime: a charter the agent follows, a memory model it keeps across runs, and the prompts to start it. They drive this tool surface, with money and go-live gates on by default.

Use the skill for a quick start in Claude Code; use the MCP server when you want typed tools, the higher-order workflows, or a client other than Claude Code; use a recipe when you want the agent to run a whole store pattern rather than answer one request at a time.

Development

npm ci
npm run build      # tsc -> dist/
npm run typecheck
npm run lint
npm test           # vitest

The image tools shell out to bundled Python scripts in python/; the imaging layer is injectable, so the tool orchestration is unit-tested with a fake and the scripts are smoke-tested directly.

Versioning & stability

The tool surface is versioned separately from the package (this is v1). When the underlying REST API evolves, the server adapts internally — the agent-facing tool names + shapes stay stable. That's the contract that lets you install once and keep working. Package releases follow Semantic Versioning; see docs/RELEASING.md.

On the hosted connector you are always on the current version, which is another reason to prefer it: new tools appear without you upgrading anything. Clients cache the tool list, so ask get_api_reference if you suspect yours has fallen behind.

License

MIT © ApparelHub. See LICENSE.