linkfox-ehunt-shopify-store-query

EHunt Shopify store query (ehunt/shopify/storeQuery）

When the LinkFox "Third-Party Data Service" MCP is available, the corresponding gateway route ehunt/shopify/storeQuery is called (MCP display name:Shopify store query, the exact tool name is subject to the tool metadata issued in the current environment). Authentication and upstream routing are handled by the gateway; if the response contains a root-level code field, whether it succeeded is subject to the actual network.

Key points

• Pagination：page starts from 1;pageSize defaults to 20 and has a maximum of 100.
• Range parameters：*Min / *Max appear in pairs (product count, ad count, monthly visits, monthly orders), forming the upstream range.
• Store age year: 1 = within the last 1 year, 2 = 12 years, 3 = 23 years, 4 = more than 3 years.
• Sorting：sortBy integer enum (0 = product count, 1 = category count, 2 = monthly visits, 3 = FB followers, 4 = Ins followers, 5 = ad count, 6 = relevance, 7 = monthly orders default);orderBy is desc(default)/asc。
• Country：country pass the country code (e.g., US、CN）。

Script (optional)

Command-line debugging:python scripts/ehunt_shopify_store_query.py '<JSON>'(requires LINKFOXAGENT_API_KEY). See the end of references/api.md .

Reference

For the input/output parameter table, see references/api.md。

Handling Large Responses

To avoid overflowing the agent context, persist the response to disk and extract only the fields you need:

python scripts/response_io.py run --script scripts/ehunt_shopify_store_query.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>"   # or --path "<JMESPath>"

Pick --out-dir outside any git working tree (e.g. /tmp/... on Unix, %TEMP%/... on Windows). Persisted responses may contain PII, pricing, or auth-sensitive data — do not commit them. Files are not auto-deleted; clean up when the task is done.

run writes the full response to a file and emits only a schema preview + file path. read projects specific fields, with --limit/--offset for slicing and --format json|jsonl|csv|table for output.

When to prefer this pattern — apply your judgment based on the response characteristics, e.g.:

• High field count per record, or fields you don't need
• Batch/paginated results (multiple items per call)
• Long-text fields (descriptions, reviews, HTML, time series)
• Output reused across later steps rather than consumed immediately

For small, single-use responses, calling the main script directly is fine.

⚠️ The preview is a truncated schema + sample, not the full data. Any field-level decision must read from the persisted file via read.