--- 格式版本: 2 标题: "Connect and test your plugin – Plugins | OpenAI Developers" 原文链接: "https://developers.openai.com/plugins/deploy/connect-chatgpt" 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 04:39:18 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: "2026-09-04T16:29:43+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: "2026-09-04T08:29:43.529Z" 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: 20 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "正文主线为OpenAI开发者文档中的插件连接与测试教程，属于How-to/Guide，不涉及超节点、AI Rack或机架级基础设施；虽来源权威（OpenAI官方），但主题完全不相关、无新增机架级事实、无商业部署信号；命中七之1教程硬否决与正文不可作为超节点情报，最高54分，实际按维度仅给0-5分。" AI质检模型: "zj-deepseek-v4-flash" AI质检时间: "2026-09-04T16:47:26+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: 5 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\",\"HTTPS\",\"HTTP\",\"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\",\"HTTPS\",\"HTTP\",\"API\"],\"rank\":-18.42431063812961},{\"id\":\"runtime-51cfe3db04ed0bd3efb5e0e3\",\"title\":\"The full stack behind abundant intelligence\",\"sourceType\":\"ai_excellent_article\",\"time\":\"2026-08-25\",\"matchedTerms\":[\"OpenAI\",\"RAS\",\"Microsoft\",\"URL\",\"HTTPS\",\"HTTP\",\"API\"],\"rank\":-12.903704262107906},{\"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\",\"HTTPS\",\"HTTP\"],\"rank\":-12.872515030312233},{\"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\",\"HTTPS\",\"HTTP\"],\"rank\":-12.387315215688663},{\"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\",\"HTTPS\",\"HTTP\"],\"rank\":-12.267406418970474}]" 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/deploy/connect-chatgpt" --- Title: Connect and test your plugin – Plugins | OpenAI Developers URL Source: https://developers.openai.com/plugins/deploy/connect-chatgpt Published Time: Fri, 04 Sep 2026 04:39:18 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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plugin](https://developers.openai.com/plugins/deploy/connect-chatgpt) * [Submit and publish](https://developers.openai.com/plugins/deploy/submission) * [Submission error reference](https://developers.openai.com/plugins/deploy/submission-errors) ### Conversion specs * [Restaurant reservation spec](https://developers.openai.com/plugins/guides/restaurant-reservation-conversion-spec) * [Get Quote spec](https://developers.openai.com/plugins/guides/local-services-request-quote-conversion-spec) * [Product checkout spec](https://developers.openai.com/plugins/guides/product-checkout-conversion-spec) ### Guides * [UI guidelines](https://developers.openai.com/plugins/concepts/ui-guidelines) * [Optimize Metadata](https://developers.openai.com/plugins/guides/optimize-metadata) * [Submit a Claude Code plugin](https://developers.openai.com/plugins/guides/submit-claude-plugin) * [Security & Privacy](https://developers.openai.com/plugins/guides/security-privacy) * [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 # Connect and test your plugin Test an optional MCP server, then package and evaluate the complete plugin. Copy Page Test each capability before testing the complete installed plugin. If the plugin includes an MCP server, start by connecting and evaluating the server in developer mode. Then package the plugin with its skills and test the complete experience. Skills-only plugins can skip the first section. Keep your evaluation prompts and results throughout development so you can compare behavior across releases. ## Test an MCP server (optional) ### Prepare the endpoint Confirm that: * The MCP server is reachable through a public HTTPS endpoint or [Secure MCP Tunnel](https://developers.openai.com/api/docs/guides/secure-mcp-tunnels). * A public endpoint supports streamable HTTP, typically at `/mcp`, or the tunnel can reach its configured stdio or HTTP MCP server. * Tool names, descriptions, schemas, and annotations are present. * Authentication discovery works for tools that require an account. Use Secure MCP Tunnel to connect a private MCP server in developer mode without exposing the server to the public internet. A development tunnel or another HTTPS forwarding service can also provide an endpoint for local testing. These testing options do not replace the public HTTPS endpoint required for [plugin submission](https://developers.openai.com/plugins/build/mcp-server#deploy-the-endpoint). ### Inspect the MCP server Use [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector) to list and call tools directly: `npx @modelcontextprotocol/inspector@latest` Exercise each tool with representative inputs, edge cases, missing identifiers, and empty results. Verify schema validation, authentication errors, annotations, confirmation behavior, and the model-readable result. ### Enable developer mode In ChatGPT: 1. Open **Settings**. 2. Select **Security and login**. 3. Turn on **Developer mode**. Developer mode availability can depend on account and workspace policy. ### Add the MCP server 1. Go to [ChatGPT Plugins](https://chatgpt.com/plugins). 2. Select the plus button. 3. Enter a user-facing name and description. 4. Under **Connection**, choose the connection method: * For a public endpoint, enter the MCP server URL, including the `/mcp` path. * For Secure MCP Tunnel, select **Tunnel**, then choose an available tunnel or enter its `tunnel_id`. 5. Create the connection. 6. Review the tools and metadata discovered from the server. If ChatGPT cannot connect, verify the public HTTPS endpoint with MCP Inspector, or check the tunnel’s workspace association and `tunnel-client` status. Resolve transport, initialization, schema, or authentication errors before continuing. ### Check tool selection Start a new conversation and add the MCP connection from the tools menu. Create an evaluation set that includes: * Direct requests that should call a specific tool. * Indirect requests that express the same goal. * Follow-up requests that reuse identifiers from earlier results. * Write actions that require authorization or confirmation. * Unsupported requests that shouldn’t call a tool. For each request, record the selected tool, arguments, result, errors, and confirmation behavior. Rerun the set whenever you change tool names, descriptions, schemas, or annotations. If the server returns optional UI, test both the component and the model-readable result. ### Test through the API Playground For raw request and response logs, open the [API Playground](https://platform.openai.com/playground): 1. Choose **Tools → Add → MCP Server**. 2. Enter the HTTPS endpoint and connect. 3. Run test prompts and inspect the request and response data. ### Refresh metadata After changing tool names, descriptions, schemas, annotations, authentication, or UI resources: 1. Deploy or restart the MCP server. 2. Open the connection at [ChatGPT Plugins](https://chatgpt.com/plugins). 3. Select **Refresh**. 4. Confirm that the advertised metadata changed. 5. Start a new conversation and rerun the affected tests. This refresh flow applies to MCP servers connected in developer mode. Published plugins with MCP use reviewed [metadata snapshots](https://developers.openai.com/plugins/deploy/submission#how-published-mcp-metadata-versions-work). To update published metadata, scan the server, submit a new version, and publish the approved version. Before packaging the plugin, confirm that: * The tool list matches the documented capabilities. * Structured results match each tool’s declared output schema. * Authentication failures return useful errors. * Positive prompts select the expected tools and negative prompts don’t. * Optional UI renders without console errors and restores state correctly. ## Test the complete plugin After the MCP server works—or immediately for a skills-only plugin—package and install the complete plugin from a local source: 1. [Package the plugin](https://developers.openai.com/plugins/build/plugins) with its skills, manifest, and MCP server connection when applicable. 2. Add the plugin to a local marketplace and install it from the Plugins Directory. 3. Start a new conversation with the plugin enabled. 4. Run representative requests from the plugin’s use-case inventory. Create an evaluation set that includes: * Direct requests that should use a skill. * Indirect requests that express the same goal. * Follow-up requests that depend on an earlier result. * Negative requests that shouldn’t use the plugin. * Boundary cases that the plugin intentionally doesn’t support. For each request, check that the plugin follows the skill instructions, uses the expected resources, completes every required step, and produces a useful result. Record any missing steps, unnecessary activations, or inconsistent results. For plugins with an MCP server, also confirm that skills invoke the right tools, tool results return to the workflow, authentication works after installation, and users can complete each combined workflow from start to finish. Before submission, confirm that: * Each skill activates for the intended requests. * Similar phrasing produces consistent behavior. * Unsupported requests don’t activate the plugin. * Bundled files and references resolve after installation. * The plugin’s starter prompts represent workflows it can complete. * For plugins with an MCP server, bundled skills and tools work together as intended. [Next Submit and publish](https://developers.openai.com/plugins/deploy/submission) Ask AI ## Docs agent Loading docs agent...