StoreSignal MCP Server

mcp-name: io.github.anthesiallc/storesignal

A Model Context Protocol server that exposes the StoreSignal API as tools, so any MCP client (Claude Desktop, Cursor, ChatGPT connectors, or an agent framework) can analyze Shopify stores and run market intelligence queries conversationally.

It's a thin wrapper: each tool maps to one StoreSignal REST endpoint. All the data work happens in the API.

Tools

Related MCP server: Clind MCP Server

Get an API key

Free tier is 250 calls/month, no credit card:

curl -X POST https://storesignal.anthesia.io/api/v1/signup \
  -H 'Content-Type: application/json' \
  -d '{"email":"you@example.com"}'

The key comes back in the api_key field of the response.

Install and run

The easiest way is with uv (no manual venv needed):

# stdio transport (default — for Claude Desktop, Cursor, most local clients)
STORESIGNAL_API_KEY=ss_your_key uvx storesignal-mcp

# streamable-HTTP transport (for remote / web clients)
STORESIGNAL_API_KEY=ss_your_key uvx storesignal-mcp --http

Or install with pip into its own environment:

pip install storesignal-mcp
STORESIGNAL_API_KEY=ss_your_key storesignal-mcp

Note: install into a dedicated environment. The mcp SDK requires a newer starlette than the StoreSignal API app pins, so the two will conflict if installed together.

Environment variables:

• STORESIGNAL_API_KEY (required) — your StoreSignal API key.

• STORESIGNAL_BASE_URL (optional) — defaults to https://storesignal.anthesia.io.

• STORESIGNAL_TIMEOUT (optional) — request timeout in seconds, default 60.

Client configuration

Claude Desktop

Add to claude_desktop_config.json (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "storesignal": {
      "command": "uvx",
      "args": ["storesignal-mcp"],
      "env": { "STORESIGNAL_API_KEY": "ss_your_key" }
    }
  }
}

Cursor

Add the same block to ~/.cursor/mcp.json (or the project .cursor/mcp.json).

Smithery (hosted, no install)

The server is hosted on Smithery, so MCP clients that support Smithery can connect without installing anything. You provide your StoreSignal API key in the Smithery config and it routes to the server.

LangChain / LangGraph

Any LangChain or LangGraph agent can use these tools through langchain-mcp-adapters:

# pip install langchain-mcp-adapters langgraph "langchain[anthropic]"
from langchain_mcp_adapters.client import MultiServerMCPClient

client = MultiServerMCPClient(
    {
        "storesignal": {
            "transport": "stdio",
            "command": "uvx",
            "args": ["storesignal-mcp"],
            "env": {"STORESIGNAL_API_KEY": "ss_your_key"},
        }
    }
)
tools = await client.get_tools()
# hand `tools` to a LangGraph/LangChain agent, e.g.
# from langgraph.prebuilt import create_react_agent
# agent = create_react_agent("anthropic:claude-opus-4-8", tools)

LlamaIndex works the same way via its MCP tool spec.

Example agent conversations

"What apps does Allbirds use?" → analyze_store("https://www.allbirds.com")

"Compare the tech stacks of Brooklinen and Bombas." → compare_stores(["https://brooklinen.com", "https://bombas.com"])

"Which Shopify stores are running Judge.me?" → find_stores_using_app("judge-me")

"What are the top email-marketing apps on Shopify?" → app_adoption(category="Email Marketing")

"Compare Klaviyo to Omnisend." → app_vs_app("klaviyo", "omnisend")

"Tell me about the Beauty vertical." → industry_overview("Beauty")

Develop from source

git clone https://github.com/anthesiallc/storesignal-mcp && cd storesignal-mcp
python -m venv .venv
.venv/Scripts/python -m pip install -e ".[http]"   # Windows; [http] adds uvicorn for --http
# .venv/bin/pip install -e ".[http]"                # macOS/Linux
STORESIGNAL_API_KEY=ss_your_key .venv/Scripts/python -m storesignal_mcp.server

Notes

• Data is extracted only from publicly accessible Shopify storefront endpoints.

• Not affiliated with Shopify Inc.

• The LLM-classification endpoints (industry / store type / growth stage) are intentionally not exposed as MCP tools. The agent calling MCP is already an LLM and can reason about the raw corpus data itself — exposing them would waste tokens on a round trip to OpenAI.