linkfox-ehunt-etsy-category-search
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。
How do I install this agent skill?
npx skills add https://github.com/linkfox-ai/linkfox-skills --skill linkfox-ehunt-etsy-category-searchIs this agent skill safe to install?
- Gen Agent Trust Hubpass
No security issues detected. This skill provides functionality to search Etsy category data via the EHunt service, using a Python script to interface with the vendor's API gateway.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
What does this agent skill do?
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。
<!-- 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_etsy_category_search.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>" # or --path "<JMESPath>"
Pick
--out-diroutside 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.
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/linkfox-ai/linkfox-skills/linkfox-ehunt-etsy-category-search">View linkfox-ehunt-etsy-category-search on skillZs</a>