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linkfox-ehunt-etsy-store-query

linkfox-ehunt-etsy-store-query

github.com/linkfox-aiMarketing & growth113130 installsUpdated 2w ago
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About linkfox-ehunt-etsy-store-query

A skill for querying Etsy store data through LinkFox's third-party data service MCP. It supports pagination, range filters, and sorting by sales, reviews, or favorites.

Key features

  1. 01Query stores with pagination
  2. 02Filter by start and end ranges
  3. 03Sort by sales, reviews, or favorites
  4. 04Sort in ascending or descending order

Use cases

  1. 01Filter Etsy stores by criteria
  2. 02Compare total and weekly store sales
  3. 03Rank stores by reviews or favorites

MCP support

Calls `_ehunt_storeQuery` through LinkFox's third-party data service MCP to query Etsy stores.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/linkfox-ai/linkfox-skills --skill linkfox-ehunt-etsy-store-query

After installing, type/linkfox-ehunt-etsy-store-queryin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
api.md
scripts/
ehunt_etsy_store_query.py
response_io.py

Details

From the project README

EHunt Etsy 店铺查询（_ehunt_storeQuery）

在具备 LinkFox「第三方数据服务」MCP 时，按工具名 _ehunt_storeQuery 调用（MCP 展示名：Etsy店铺查询，以当前环境下发的工具元数据为准）。鉴权与上游路由由网关处理；若响应含根级 code 字段，是否成功以实网为准。

要点

  • 分页：page 从 1 起；pageSize 默认 20、最大 100。
  • 区间入参：begin* / end* 成对对应上游逗号范围；只填一侧时上游为「起始~」或「~结束」。
  • 排序：sortBy 仅 8~11（8 总销量、9 周销量、10 评论数、11 收藏数）。sortDesc：1=降序，0=升序（勿与商品接口的 sortDesc 混用）。

脚本（可选）

命令行调试：python scripts/ehunt_etsy_store_query.py '<JSON>'（需 LINKFOXAGENT_API_KEY）。详见 references/api.md 末尾。

参考

入参/出参表见 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_etsy_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.

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