| name | linkfox-ehunt-etsy-category-search |
| description | 通过 `_ehunt_etsyCategorySearch`（MCP 展示名「Etsy品类查询」）在已同步到 MCP 库的 EHunt Etsy 类目数据中检索类目名称、id 与 parentIds，用于商品/店铺筛选的类目 id。当用户提到 EHunt Etsy 类目、Etsy category id、Etsy 类目树、_ehunt_etsyCategorySearch、Etsy品类查询、syncEtsyCategory / _ehunt_syncEtsyCategory（Etsy品类同步）后查类目 时触发。即使用户未写 EHunt，只要在本地已同步的 Etsy 类目库里按关键词找类目 id，也应触发此技能。 |
EHunt Etsy 类目检索（_ehunt_etsyCategorySearch）
在具备 LinkFox「第三方数据服务」MCP 时，按工具名 _ehunt_etsyCategorySearch 调用（MCP 展示名：Etsy品类查询，以当前环境下发的工具元数据为准）。数据来自 MCP 库本地检索。
前置条件
库内须已有 _ehunt_syncEtsyCategory 写入的全量类目（MCP 展示名：Etsy品类同步）。若无数据或结果为空，应先完成同步再检索。
要点
- 必填：
keyword（子串匹配类目名称、类目 id、parentIds）。
- 分页：
page 从 1 起；pageSize 默认 50、最大 200。
- 返回的
id 可作为 _ehunt_productQuery / 店铺侧 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_etsy_category_search.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_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.
