通过 EHunt Temu 品类检索(网关路由 `ehunt/temu/temuCategorySearch`)在已同步到本地库的 EHunt Temu 类目数据中按关键词检索类目中文名、英文名与类目 id,用于商品/店铺筛选的类目 id。当用户提到 EHunt Temu 类目、Temu category id、...
--- name: linkfox-ehunt-temu-category-search version: 1.0.0 category: product-sourcing description: 通过 EHunt Temu 品类检索(网关路由 `ehunt/temu/temuCategorySearch`)在已同步到本地库的 EHunt Temu 类目数据中按关键词检索类目中文名、英文名与类目 id,用于商品/店铺筛选的类目 id。当用户提到 EHunt Temu 类目、Temu category id、Temu 类目树、Temu 后台类目、temu 品类、syncTemuCategory(Temu 品类同步)后查类目、Temu category search 时触发。即使用户未写 EHunt,只要在本地已同步的 Temu 类目库里按关键词找类目 id,也应触发此技能。 --- # EHunt Temu 类目检索(`ehunt/temu/temuCategorySearch`) 在具备 LinkFox「第三方数据服务」MCP 时,对应网关路由 **`ehunt/temu/temuCategorySearch`** 调用(MCP 展示名:**Temu 品类查询**,确切工具名以当前环境下发的工具元数据为准)。数据来自 **本地库检索**。 ## 前置条件 库内须已有 **`ehunt/temu/syncTemuCategory`**(MCP 展示名:**Temu 品类同步**)写入的全量类目。若无数据或结果为空,应先完成同步再检索。 ## 要点 - **必填**:`keyword`(子串匹配类目中文名、英文名、类目 id)。 - **分页**:`page` 从 1 起;`pageSize` 默认 50、最大 200。 - 返回的 **`id` / `categoryId`** 可作为 Temu 商品查询的 `categoryHome`/`categoryBackend`、店铺查询的 `category` 等入参的类目标识(与具体工具 schema 一致即可)。 ## 脚本(可选) 命令行调试:`python scripts/ehunt_temu_category_search.py '<JSON>'`(需 `LINKFOXAGENT_API_KEY`)。详见 [references/api.md](references/api.md) 末尾。 ## 参考 入参/出参表见 [references/api.md](references/api.md)。 <!-- LF_LARGE_RESPONSE_BLOCK --> ## 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_category_search.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`. <!-- /LF_LARGE_RESPONSE_BLOCK -->
don't have the plugin yet? install it then click "run inline in claude" again.