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linkfox-ehunt-temu-store-query

linkfox-ehunt-temu-store-query

github.com/linkfox-aiMarketing & growth11389 installsUpdated 2w ago
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About linkfox-ehunt-temu-store-query

A LinkFox skill for querying Temu store data through the EHunt storeQuery route. It offers range filters, site and category selection, sorting, pagination, and selective reading of saved large responses.

Key features

  1. 01Filter stores by sales, revenue, rating, followers, and product count
  2. 02Select sites, categories, and managed-store mode
  3. 03Support pagination and configurable sorting
  4. 04Save large responses and read selected fields

Use cases

  1. 01Find stores within sales or revenue ranges
  2. 02Compare stores by rating, followers, or product count
  3. 03Review chosen fields from paginated store results

MCP support

Uses the LinkFox third-party data service MCP to call the `ehunt/temu/storeQuery` gateway route. Exact tool name depends on the metadata supplied in the current environment.

Install

Copy the command and run it in a terminal.

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

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

Bundled files

References and scripts installed along with SKILL.md.

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

Details

From the project README

EHunt Temu 店铺查询（ehunt/temu/storeQuery）

在具备 LinkFox「第三方数据服务」MCP 时，对应网关路由 ehunt/temu/storeQuery 调用（MCP 展示名：Temu 店铺查询，确切工具名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：*Min / *Max 成对出现（总/周/月销量、总/周/月销售额、评分、评论、粉丝、商品数），组成上游区间。
  • 站点：siteId 国家站点 ID，多个逗号分隔（如 211=美国、76=英国）。
  • 类目：category 后台类目 ID，多个逗号分隔。
  • 托管模式：isLocal（0=全托管，1=半托管，字符串）。
  • 开店时间：listedTimeBegin / listedTimeEnd（YYYY-MM-DD）。
  • 排序：sortBy 为「字段-方向」字符串，如 order_week_count-0（周销量降序，默认）、order_count-0、total_revenue-0、rating-0。

脚本（可选）

命令行调试：python scripts/ehunt_temu_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_temu_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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