--- 格式版本: 2 标题: "Optimize Metadata – Plugins | OpenAI Developers" 原文链接: "https://developers.openai.com/plugins/guides/optimize-metadata" 发布日期: "2026-09-04" 发布时间校准状态: "found" 发布时间需复核: "否" 发布时间来源: "rule:local:strict_original_body" 发布时间证据: "Published Time: Fri, 04 Sep 2026 04:44:22 GMT" 发布时间校准原因: "规则确认唯一严格发布时间，来源 local:strict_original_body" 发布时间校准置信度: "high" 发布时间候选数量: 4 发布时间严格候选数量: 1 发布时间原页读取状态: "source template page reused from URL open" 发布时间未找到原因: "" 发布时间校准时间: "2026-09-04T16:30:12+08:00" 发布时间仲裁状态: "skipped" 发布时间仲裁尝试次数: 0 发布时间仲裁耗时毫秒: 0 发现时间: "2026-09-04T16:24:00+08:00" 入库时间: "2026-09-04T08:30:12.215Z" 来源平台: "固定入口" 搜索渠道: "fixed_url" 搜索词: "https://developers.openai.com/blog" 匹配关键词: - "deployment" - "performance" - "latency" - "throughput" - "AI" 相关厂家: - "OpenAI" - "Microsoft" - "AWS" - "Google" - "Oracle" 相关专家: [] 内容类型: "网页" 抓取工具: "Jina Reader" 清洗工具: "Jina Reader Markdown + Defuddle/Readability 正文提取" 原始附件: [] AI优质: "否" AI打分: 23 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "正文为OpenAI官方开发者文档中《Optimize Metadata》指南，主线是优化ChatGPT/Codex插件元数据（名称、描述、参数文档）以改进工具调用，完全不涉及超节点、AI Rack、机架级系统或关键部件，主题无关。来源权威但内容属教程/指南，无新架构、新标准、新产品或部署信号。命中硬否决第1条（教程与运维选型），非优质。" AI质检模型: "zj-deepseek-v4-flash" AI质检时间: "2026-09-04T16:47:24+08:00" AI主题相关性: 0 AI来源权威性: 15 AI新颖性: 0 AI技术细节: 0 AI商业部署信号: 0 AI完整性: 8 AI评分提示词版本: "v17-精简生产版" AI评分提示词SHA256: "48fb9777f386026761b4873eaff30807694fb11e9b352d7c69bf2dfde750cc7d" AI评分知识库版本: "knowledge_base_v1-20260819+runtime.87" AI评分知识库SHA256: "0cf9900cb3e9d03b3f4c39f45979aac6e2d5c789a68d1b2802c9dab62cd2710e" AI评分知识库检索词: "[\"OpenAI\",\"https://developers.openai.com/blog\",\"RAS\",\"NPU\",\"Google\",\"Meta\",\"Microsoft\",\"URL\",\"GMT\",\"UI\",\"IDE\",\"API\"]" AI评分知识库命中: "[{\"id\":\"july-correct-0033\",\"title\":\"Microsoft, Alphabet, Meta Pivot from Buy to Build in AI\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OpenAI\",\"RAS\",\"Google\",\"Meta\",\"Microsoft\",\"URL\",\"UI\",\"IDE\",\"API\"],\"rank\":-18.229700393321},{\"id\":\"july-correct-0001\",\"title\":\"全球首颗2nm GPU来了！苏姿丰甩出“最强AI机架”，CPU性能干翻英伟达 - 智东西\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OpenAI\",\"RAS\",\"NPU\",\"Meta\",\"IDE\"],\"rank\":-12.752907321262027},{\"id\":\"runtime-51cfe3db04ed0bd3efb5e0e3\",\"title\":\"The full stack behind abundant intelligence\",\"sourceType\":\"ai_excellent_article\",\"time\":\"2026-08-25\",\"matchedTerms\":[\"OpenAI\",\"RAS\",\"Microsoft\",\"URL\",\"UI\",\"IDE\",\"API\"],\"rank\":-12.679167019868977},{\"id\":\"july-correct-0018\",\"title\":\"AMD, Cerebras partner on joint Helios rack-scale AI inference platform\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OpenAI\",\"RAS\",\"Meta\",\"Microsoft\",\"URL\",\"IDE\"],\"rank\":-12.180948954382538},{\"id\":\"july-correct-0015\",\"title\":\"AMD to join the optical interconnect party with 2027 Instinct GPUs\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OpenAI\",\"Meta\",\"Microsoft\",\"URL\",\"IDE\"],\"rank\":-12.059703544606952}]" 采集批次: "2026年9月4日15点53分07秒" 采集批次ID: "20260904-155307-58ad1755" 去重键: "https://developers.openai.com/plugins/guides/optimize-metadata" --- Title: Optimize Metadata – Plugins | OpenAI Developers URL Source: https://developers.openai.com/plugins/guides/optimize-metadata Published Time: Fri, 04 Sep 2026 04:44:22 GMT Markdown Content: For the complete documentation index, see [llms.txt](https://developers.openai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to the page URL. 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[Troubleshooting](https://developers.openai.com/plugins/deploy/troubleshooting) ### Resources * [Changelog](https://developers.openai.com/plugins/changelog) * [Plugin guidelines](https://developers.openai.com/plugins/app-guidelines) * [MCP server review requirements](https://developers.openai.com/plugins/deploy/app-review) * [Plugin UI reference](https://developers.openai.com/plugins/reference) * [Checkout API reference](https://developers.openai.com/plugins/build/monetization) Copy Page # Optimize Metadata Improve discovery and behavior with rich metadata. Copy Page ## Why metadata matters ChatGPT and Codex decide when to call your tool based on the metadata you provide. Well-crafted names, descriptions, and parameter docs increase recall on relevant prompts and reduce accidental activations. Treat metadata like product copy—it needs iteration, testing, and analytics. ## Gather a golden prompt set Before you tune metadata, assemble a labelled dataset: * **Direct prompts:** users explicitly name your product or data source. * **Indirect prompts:** users describe the outcome they want without naming your tool. * **Negative prompts:** cases where built-in tools or other tools should handle the request. Document the expected behaviour for each prompt (call your tool, do nothing, or use an alternative). You will reuse this set during regression testing. ## Draft metadata that guides the model For each tool: * **Name:** pair the domain with the action (`calendar.create_event`). * **Description:** start with “Use this when…” and call out disallowed cases (“Do not use for reminders”). * **Parameter docs:** describe each argument, include examples, and use allowed values for constrained inputs. * **Read-only hint:** annotate `readOnlyHint: true` on tools that only retrieve or compute information and never create, update, delete, or send data outside the conversation. * For tools that are not read-only: * **Destructive hint** - annotate `destructiveHint: false` on tools that do not delete or overwrite user data. * **Open-world hint** - annotate `openWorldHint: false` on tools that do not publish content or reach outside the user’s account. ## Evaluate in developer mode 1. In ChatGPT, turn on Developer mode from **Settings → Security and login**, then register your MCP server at [ChatGPT Plugins](https://chatgpt.com/plugins). 2. Run through the golden prompt set and record the outcome: which tool was selected, what arguments were passed, and whether the component rendered. 3. For each prompt, track precision (did the right tool run?) and recall (did the tool run when it should?). If the model picks the wrong tool, revise the descriptions to emphasise the intended scenario or narrow the tool’s scope. ## Iterate methodically * Change one metadata field at a time so you can attribute improvements. * Keep a log of revisions with timestamps and test results. * Share diffs with reviewers to catch ambiguous copy before you deploy it. After each revision, repeat the evaluation. Aim for high precision on negative prompts before chasing marginal recall improvements. ## Production monitoring Once your connector is live: * Review tool-call analytics weekly. Spikes in “wrong tool” confirmations usually indicate metadata drift. * Capture user feedback and update descriptions to cover common misconceptions. * Schedule periodic prompt replays, especially after adding new tools or changing structured fields. Treat metadata as a living asset. The more intentional you are with wording and evaluation, the easier discovery and invocation become. Ask AI ## Docs agent Loading docs agent...