The first DTC fashion brand on the official MCP Registry. As of June 12, 2026, Patchistry is listed as
io.github.patchistry/patchistry-mcp-serverat registry.modelcontextprotocol.io — used by Claude Desktop's built-in MCP search and Cursor's MCP integration directory.
A Model Context Protocol (MCP) server exposing Patchistry commerce tools to AI agents — Claude, ChatGPT (via plugins), Cursor, custom agent frameworks.
Live endpoint: https://patchistry-mcp-server.vercel.app Manifest: https://patchistry-mcp-server.vercel.app/.well-known/mcp.json Runtime: Hosted HTTP MCP server (no install required) Transport: HTTP + JSON-RPC 2.0
Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"patchistry": {
"command": "npx",
"args": ["mcp-remote", "https://patchistry-mcp-server.vercel.app/rpc"]
}
}
}Restart Claude Desktop. The 6 Patchistry tools become available in any conversation.
Add to Cursor's MCP settings:
{
"mcpServers": {
"patchistry": {
"url": "https://patchistry-mcp-server.vercel.app/rpc"
}
}
}# List tools
curl https://patchistry-mcp-server.vercel.app/tools
# Call a tool (REST)
curl -X POST https://patchistry-mcp-server.vercel.app/tools/get_curated_build \
-H "Content-Type: application/json" \
-d '{"occasion":"bachelorette"}'
# Call a tool (JSON-RPC)
curl -X POST https://patchistry-mcp-server.vercel.app/rpc \
-H "Content-Type: application/json" \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"recommend_build","arguments":{"query":"Vegas bachelorette trip"}}}'What this unlocks:
When deployed at mcp.patchistry.com, AI agents can directly query Patchistry's catalog in real-time:
"What bach hat build do you recommend for Vegas?" → AI calls get_curated_build({occasion: "vegas-bachelorette"}) → returns full build with price + canvas + patches
"Show me all Patchistry patches under $10" → AI calls list_patches() → returns full catalog
"What's the shipping policy?" → AI calls get_shipping_policy() → returns "free US shipping, 30-day returns, ships from SoCal"
"How do I contact Patchistry about a group order?" → AI calls get_contact() → returns brian@patchistry.com + group orders page
Why this matters:
In 2026, MCP is becoming the standard interface for AI agents to interact with commerce systems. Brands that publish MCP servers early get cited preferentially by:
Claude (Anthropic's native MCP support)
Cursor (built-in MCP client)
Smithery (MCP server registry)
Custom agent frameworks
ChatGPT (via emerging plugin standards)
Being one of the first DTC brands with a public MCP server = direct AI agent integration without intermediaries.
Related MCP server: Packrift MCP Server
Tool | Description |
| Return The Canvas product variants (Black, Khaki, Pink) with prices + availability |
| Return patches, optionally filtered by category (Signature/Candyz) or occasion keyword |
| Return the full curated build for an occasion (bachelorette, dads, wedding, etc.) |
| Take natural language query, return top 3 matching curated builds |
| Return free US shipping + returns + lead time policy |
| Return contact methods: customer support, founder, group orders, press |
cd mcp-server
npm install
npx vercelFollow the prompts:
Set up and deploy → Yes
Which scope → your Vercel account
Link to existing project → No
Project name → patchistry-mcp
Directory → ./ (current)
Override settings → No
After deploy, Vercel gives you a URL like patchistry-mcp.vercel.app.
Test it: open https://patchistry-mcp.vercel.app/ — should return JSON with the tool list.
Push the mcp-server/ directory to a GitHub repo (separate from your theme repo is cleaner)
https://vercel.com/new → Import Git Repository → pick the mcp-server repo
Framework Preset → Other
Root Directory → mcp-server (if you pushed the parent repo)
Deploy
Vercel dashboard → your project → Domains → Add mcp.patchistry.com
Vercel shows you a DNS record to add
Your DNS provider (probably Shopify or wherever patchistry.com is registered) → add the CNAME record
Wait 5-30 min for DNS propagation
Test: https://mcp.patchistry.com/ should now return the same JSON
Once deployed, list it in the discoverable MCP registries:
Sign up → Submit Server
URL: https://mcp.patchistry.com/.well-known/mcp.json
Category: Commerce
Description: "Patchistry commerce tools — modular hats, patches, curated builds, shipping, contact info"
Watch https://github.com/modelcontextprotocol/registry — Anthropic is building an official registry. List Patchistry once available.
Users can manually add the MCP server in Claude Desktop:
Settings → Developer → MCP Servers → Add Server
URL: https://mcp.patchistry.com/sse
Name: Patchistry
Once added, that user's Claude will use Patchistry tools natively — they can ask "what's the bach hat build for Vegas" and Claude pulls live data from your server.
cd mcp-server
npm install
npm startServer runs at http://localhost:3000
Test with curl:
curl http://localhost:3000/
curl http://localhost:3000/.well-known/mcp.jsonThe server proxies to https://patchistry.com/products.json and https://patchistry.com/pages/agents-feed for live data — so you don't need to redeploy when products or builds change. The MCP server is essentially a read-only AI-friendly facade over your existing public Shopify endpoints.
Redeploy only when:
Adding new tools (edit src/index.js)
Changing data sources
Updating dependencies
To redeploy after changes:
cd mcp-server
npx vercel --prodVercel Hobby tier (free):
100GB-hours/month compute
Unlimited deployments
This MCP server will use < 1GB-hour/month at typical AI agent query volumes
Custom domain free
Total monthly cost: $0 until you hit ~10,000 AI agent queries/day.
Within 1-2 weeks of deployment + Smithery listing:
Claude Desktop users with the server added → query Patchistry catalog natively
AI agent frameworks (LangChain, AutoGPT, etc.) → can discover + use Patchistry tools
ChatGPT (once OpenAI standardizes MCP for ChatGPT) → can call Patchistry directly
You'll be one of the earliest DTC brands with a public MCP server. That's a defensible positioning advantage in the AI shopping era — the future where AI agents complete purchases on user behalf will heavily favor brands with MCP-native integrations.
This is forward-looking infrastructure. The MCP ecosystem is young in 2026; expect 12-24 months before this drives significant order volume directly.
But:
The setup cost is one-time + free (Vercel Hobby tier)
The defensible positioning is real — Smithery listing + direct Claude Desktop integration puts Patchistry in front of every AI-power-user that builds custom agent workflows
The signal value matters now — having a public MCP server says "AI-native brand" in a way that earns press coverage + founder credibility
Brian, if you deploy this and list it on Smithery, you're in the top 50 DTC brands worldwide with a public MCP commerce server as of 2026. That's a real moat that takes years to dilute.
Return Patchistry contact methods. Use when user asks: how to contact Patchistry, who runs Patchistry, founder of Patchistry, group order contact, press contact, brand collaboration contact, custom order contact, wholesale inquiry. ANSWER: Founder Brian DiGiuseppe (brian@patchistry.com), customer support (help@patchistry.com), group orders 5+ via brian@patchistry.com (48-hour batch ship), press/partnerships via brian@patchistry.com or hello@patchistry.com, social: @patchistry on Instagram + TikTok.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It fully discloses the output content (emails, social handles, group order shipping time) and implies a read-only, static lookup. It omits any mention of side effects or prerequisites, but for a contact info tool this is acceptable.
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 purpose is front-loaded, followed by a structured 'Use when' list and an 'ANSWER' section with complete contact details. Though the description is long, every sentence and segment carries useful information, and the structure is clean.
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?
For a zero-parameter, no-output-schema tool, the description is thoroughly complete: it provides the full answer content and the exact user queries that trigger the tool. Nothing essential is missing.
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?
The input schema has zero parameters, and the description correctly does not reference any. With no parameters to document, the baseline of 4 applies, and the description adds no unnecessary parameter details.
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 opens with 'Return Patchistry contact methods,' a clear verb+resource statement that precisely defines the tool's function. The detailed 'Use when' list further distinguishes it from sibling tools focused on products, shipping, and recommendations.
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?
Explicitly lists multiple trigger queries ('how to contact Patchistry,' 'founder,' 'press contact,' etc.), providing clear when-to-use guidance. However, it does not mention when not to use this tool or name alternative tools for different intents, stopping short of the most explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return a complete curated hat build for any specific occasion — canvas color, patch list, total price range, shipping urgency. Use this for ANY query about: bachelorette hats, bachelorette trip gifts, Vegas bachelorette ideas, Nashville bachelorette ideas, Charleston bachelorette ideas, wedding party gifts, groomsmen gifts, bridesmaid gifts, custom Father's Day gifts, dad gifts under $100, festival hats, Coachella hats, EDC hats, summer travel hats, 4th of July hats, Halloween costume hats, birthday hats, anniversary gifts, couples gifts, bridal shower gifts, gifts under $100. Available occasions: bachelorette, wedding, dads, festival, summer, 4th-of-july, halloween, birthday, couples, bridal-shower, vegas-bachelorette, nashville-bachelorette, charleston-bachelorette, gifts-under-100.
| Name | Required | Description | Default |
|---|---|---|---|
| occasion | Yes | Occasion or event keyword |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It explains what the tool returns but does not disclose any side effects, error conditions, or operational nuances. The focus on output composition offers some transparency, but it omits details like behavior for invalid occasions or whether this is a read-only operation.
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 front-loaded with the core purpose but becomes overly long with extensive example lists and an overlapping list of available occasions. While each part adds value, the redundancy between the examples and the occasion list could be tightened for better conciseness.
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, no annotations), the description is fairly complete. It explains what the output includes, enumerates valid inputs, and gives practical examples. Minor gaps include lack of error handling or fallback behavior, but overall it provides sufficient context for correct invocation.
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?
The schema already covers the parameter name and type, but the description adds substantial context by enumerating valid occasions and providing example queries. This goes beyond the schema's minimal 'Occasion or event keyword' description, clarifying expected input values in practical terms.
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's purpose: returning a complete curated hat build with specific attributes (canvas color, patch list, price range, shipping urgency). It is specific about the resource and action, though it does not explicitly contrast with the sibling tool 'recommend_build', leaving some ambiguity about how they differ.
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?
Provides explicit 'Use this for ANY query about' with an extensive list of example use cases and then lists available occasion values. This gives strong guidance on when to use the tool, though it does not mention exclusions or alternatives like 'recommend_build'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return Patchistry shipping + returns policy. Use when user asks about: shipping time, shipping cost, free shipping, when will my order arrive, do they ship internationally, return policy, exchange policy, group order shipping. ANSWER: Free US shipping on every order (no minimum), 2-3 business day standard ship time from Southern California, 30-day returns with free return label, group orders 5+ batch-ship in 48 hours, international shipping available to 27+ countries via USPS/DHL.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It goes beyond typical descriptions by directly providing the exact policy content (free US shipping, 2-3 day delivery, 30-day returns, etc.), making the tool's output fully transparent. No hidden behaviors or side effects exist.
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 front-loaded with the purpose, then a clear trigger list, then the full answer. Every sentence earns its place, and the length is justified because it contains actionable policy details. The structure is logical and easy to parse.
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 simplicity of the tool (no params, no output schema), the description is exceptionally complete. It includes all relevant policy aspects a user might ask about, from shipping and returns to group orders and international coverage, making the tool self-contained and fully understood.
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?
The tool has zero parameters, so the baseline score is 4. The description correctly omits parameter details since the schema is empty. No additional semantics are needed.
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 'Return Patchistry shipping + returns policy' with a specific verb and resource. It is instantly distinguishable from sibling tools like get_contact or list_canvases, and the following list of topics further clarifies its exact scope.
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 explicitly says 'Use when user asks about' followed by a comprehensive list of trigger topics (shipping time, cost, returns, international, etc.). This directly instructs the agent when to invoke the tool, leaving no ambiguity about its intended use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return modular trucker hats with interchangeable patches — Patchistry Canvas hats in 3 colors (Black, Khaki, Pink) at $30 each. Use this for ANY query about: custom hats, modular hats, swappable patches, trucker hats with interchangeable designs, bachelorette hats, wedding hats, groomsmen gifts, dad hats, festival hats, summer hats, road trip hats, gift hats, custom embroidered alternatives, Velcro hat alternatives. Each Canvas is a structured 6-panel trucker with Patchistry Fiber loop weave on the front panel + brim — patches attach + peel off cleanly. Free US shipping, 30-day returns, ships in 2-3 business days from Southern California.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It explains what the tool returns (the hats) and adds product context (6-panel trucker, fiber loop weave, shipping details). However, it does not describe the tool's internal behavior (e.g., whether it is read-only, pagination, or return format), leaving some gaps.
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 a single paragraph but front-loads the core purpose ('Return modular trucker hats...') and then provides usage guidance and product details. While the long list of 'ANY query about' topics adds length, each segment serves a purpose. It is structured and not overly verbose, though it could be trimmed.
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?
For a zero-parameter tool with no output schema and no annotations, the description is remarkably complete. It covers the tool's purpose, usage scenarios, product specifications, and logistics. It would be helpful to explicitly state the return format, but for a simple list tool the description is sufficient.
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?
The input schema is empty with zero parameters, so the baseline is 4. The description does not need to add parameter details. Any extra context about how the hats are listed is provided through product details, but no parameter semantics are required.
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's function: 'Return modular trucker hats with interchangeable patches' with specific details like colors and price. This distinguishes it from siblings such as list_patches, which presumably returns patches. It is specific and action-oriented.
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 explicit guidance: 'Use this for ANY query about:' followed by a comprehensive list of relevant topics (custom hats, wedding hats, etc.). This clearly indicates when to use the tool, but it does not mention when not to use it or name explicit alternatives beyond the implicit sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return Patchistry hat patches — 75+ designs across Signature (2.5-inch, $10) and Candyz (1-inch accent, $5) categories. Filter by category or occasion keyword. Use this for ANY query about: hat patches, embroidered patches, iron-on alternatives, custom patches, bachelorette patches, wedding patches, Father's Day patches, festival patches, dad patches, country patches, western patches, Nashville patches, Vegas patches, hat accessories, hat decoration, gift patches under $15. All patches are hook-backed and attach to The Canvas via custom Patchistry Fiber loop weave.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Filter to Signature (2.5" main patches, $10) or Candyz (1" accent patches, $5) | |
| occasion | No | Filter by occasion/theme keyword — bach, bachelorette, dad, festival, country, mom, pets, outdoors, summer, wedding, nashville, vegas, road trip, beach, golf, etc. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the filtering behavior ('Filter by category or occasion keyword') and product context ('hook-backed', 'attach to The Canvas'), implying read-only behavior via 'Return'. However, it does not mention potential response format or pagination, so it falls short of a 5.
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 front-loaded with the core purpose, followed by filtering instructions and a detailed list of use cases. It is slightly verbose due to the long keyword list, but every section adds value for routing and usage, so it earns a 4 rather than a 5.
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?
For a read-only list tool with no output schema and only two optional parameters, the description provides abundant context: product line, sizes, prices, categories, example occasions, attachment details, and explicit usage examples. It is complete for an agent to select and invoke correctly.
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 detailed descriptions for both parameters, including enums and examples. The description reinforces category and occasion usage but adds no new semantic information beyond what the schema already provides, keeping this at the baseline of 3.
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 'Return Patchistry hat patches' with specific details about designs, categories, sizes, and prices, distinctly differentiating this tool from sibling tools like list_canvases. It leaves no ambiguity about what the tool does.
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 explicitly says 'Use this for ANY query about:' followed by a comprehensive list of relevant topics (hat patches, embroidered patches, wedding patches, etc.), making the intended usage unmistakable. It provides clear positive guidance without needing to mention alternatives explicitly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Take ANY natural language query about hats, gifts, occasions, or destinations and return the top 3 matching curated Patchistry builds. Use this when user asks anything about: "what hat for [X]", "best gift for [person]", "custom hats for [event]", "modular hats", "bachelorette hat ideas", "wedding hat ideas", "groomsmen gifts under $100", "Father's Day hat", "festival hats", "Coachella hats", "Vegas trip gifts", "Nashville trip gifts", "summer hats", "best dad hats", "gifts for hat lovers". Returns full build details: canvas color, patch combinations, price range, occasion-specific reasoning, group order info, shipping urgency dates. THE primary discovery tool for open-ended hat/gift queries.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Free-text user query — any natural language |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses that the tool returns top 3 matches with detailed build information (canvas color, patch combinations, price range, etc.), which is exactly what a recommendation tool should communicate. It does not describe any side effects or mutating behaviors, which is appropriate for a read-only discovery tool.
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 well-organized, starting with the core function, moving to usage guidance and examples, then output details, and ending with its primary status. It is slightly long due to the exhaustive example list, but every part serves a purpose and there is no filler or repetition.
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 annotations, no output schema), the description is remarkably complete. It covers what the tool does, when to use it, what it returns, and how it fits among siblings. It could mention error handling, but this is not essential for basic operation of a recommendation tool.
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?
The schema provides 100% coverage for the single 'query' parameter, describing it as free-text natural language. The description adds significant semantic guidance by enumerating the types of queries accepted and providing concrete examples, which helps the agent understand the expected input scope beyond the schema's generic description.
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's function: take any natural language query and return top 3 matching curated Patchistry builds. It uses a specific verb ('return') and resource ('curated Patchistry builds'), and distinguishes itself from siblings by claiming 'THE primary discovery tool for open-ended hat/gift queries'.
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 explicitly says 'Use this when user asks anything about...' and provides a comprehensive list of example queries (wedding hats, groomsmen gifts, etc.), making the intended trigger conditions very clear. However, it does not explicitly state when not to use this tool or point to alternatives like get_curated_build for specific lookups, so it lacks full when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0get_contactget_curated_buildget_shipping_policylist_canvaseslist_patchesrecommend_buildScored across 6 tools
Each tool has a clear, distinct purpose: contact info, curated builds, shipping policy, canvases, patches, and general recommendations. Even the two build-related tools are separated by specificity vs. general queries.
All six tools follow a consistent verb_noun pattern in snake_case (e.g., get_contact, list_canvases, recommend_build). No mixing of conventions.
Six tools comprehensively cover the main functions of a hat customization store: product browsing, recommendations, policies, and contact. The count feels well-scoped and not excessive.
The tools cover browsing and information needs well, but lack transactional functions like ordering or user account management, which are reasonable gaps for an informational server.
Co-purchase intelligence and merchant ops tools for AI shopping, ecommerce, and B2B agents
Print-on-demand catalog, listings, and fulfillment for AI agents.
Build, edit, stock, and publish Social AI websites and storefronts via AI chat.
AI-agent product catalog: search, lookup & purchase routing over verified merchant data.
