--- 格式版本: 2 标题: "Build an MCP server – Plugins | OpenAI Developers" 原文链接: "https://developers.openai.com/plugins/build/mcp-server" 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 04:44:55 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: "2026-09-04T16:29:14+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: "2026-09-04T08:29:14.179Z" 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" - "Meta" - "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: 25 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，主题是构建MCP服务器，完全不涉及超节点、AI Rack、机柜级系统或相关关键部件，无任何rack-scale基础设施信息。来源权威性虽高，但主题相关性为零，无新增硬件/标准/部署事实，命中硬否决项教程类文档，总分低于55，判定非优质。" AI质检模型: "zj-deepseek-v4-flash" AI质检时间: "2026-09-04T16:39:33+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: 10 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\",\"MCP\",\"URL\",\"GMT\",\"UI\",\"HTTP\"]" 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\",\"HTTP\"],\"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\",\"HTTP\"],\"rank\":-12.752907321262027},{\"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\",\"HTTP\"],\"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\",\"HTTP\"],\"rank\":-12.059703544606952},{\"id\":\"july-correct-0112\",\"title\":\"超前点映AMD Advancing AI 2026：AMD AI YES？-36氪\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OpenAI\",\"Meta\",\"Microsoft\",\"URL\",\"UI\",\"HTTP\"],\"rank\":-11.753070973451914}]" 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/build/mcp-server" --- Title: Build an MCP server – Plugins | OpenAI Developers URL Source: https://developers.openai.com/plugins/build/mcp-server Published Time: Fri, 04 Sep 2026 04:44:55 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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Copy Page Add an MCP server when a plugin use case needs live data, authentication, controlled actions, or code that runs on infrastructure you operate. The server defines the tools available to ChatGPT and Codex. It does not need to return custom UI. Start from the supported goals in your [use-case inventory](https://developers.openai.com/plugins/plan/use-case). Each tool should help complete a recognizable user goal and should expose only the data and actions required for that goal. Build the tools first. After the server works without custom UI, you can [add UI to the MCP server](https://developers.openai.com/plugins/build/chatgpt-ui) for workflows that need visual interaction. ## Choose an MCP software development kit The official software development kits provide schema helpers, server scaffolding, and streamable HTTP transport: * [TypeScript SDK](https://github.com/modelcontextprotocol/typescript-sdk), published as `@modelcontextprotocol/sdk`. * [Python SDK](https://github.com/modelcontextprotocol/python-sdk), published as `mcp`. Install the SDK that matches your server stack: ``` # TypeScript npm install @modelcontextprotocol/sdk zod # Python pip install mcp ``` ## Create the server Create an MCP server with a stable name and version: ``` import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js"; const server = new McpServer({ name: "acme-projects", version: "1.0.0", }); ``` MCP servers can also return an [`instructions` field](https://modelcontextprotocol.io/specification/2025-06-18/basic/lifecycle#initialization) during initialization. ChatGPT and Codex use these instructions alongside tool metadata. Use server instructions for guidance that applies across tools, such as required tool sequences or shared rate limits. Keep the most important details in the first 512 characters. Do not repeat every tool description or try to change the model’s personality. ``` const server = new McpServer( { name: "acme-projects", version: "1.0.0" }, { instructions: "Before updating a project, call get_project to confirm its ID and current status.", } ); ``` ## Define tools from user goals Create one tool for each distinct action the plugin must support. Prefer focused operations such as `list_projects`, `get_project`, and `update_project` over one tool with many unrelated modes. Each tool needs: * An action-oriented name and human-readable title. * A description that explains when to use it. * An explicit input schema. * An output schema when the tool returns structured data. * Accurate safety annotations. * A handler that authorizes the request and performs the operation. The model uses this metadata to decide whether and how to call the tool. Treat names, descriptions, schemas, and annotations as part of the plugin’s user-facing behavior. ``` import { z } from "zod"; server.registerTool( "list_projects", { title: "List projects", description: "Use this when the user wants to find or review projects in their Acme workspace.", inputSchema: { status: z.enum(["active", "archived"]).optional(), }, outputSchema: { projects: z.array( z.object({ id: z.string(), name: z.string(), status: z.string(), }) ), }, annotations: { readOnlyHint: true, openWorldHint: false, destructiveHint: false, }, }, async ({ status }) => { const projects = await listProjects({ status }); return { structuredContent: { projects }, content: [ { type: "text", text: `Found ${projects.length} projects.`, }, ], }; } ); ``` ## Return useful results without UI A tool result can include: * `structuredContent`: concise data the model can inspect and use in later calls. * `content`: text or other MCP content that helps the model answer the user. * `_meta`: client-specific data hidden from the model. Return enough information for the model to complete the workflow without a component. Use stable identifiers in structured results so later tools can refer to the same records. Do not put secrets, access tokens, or unnecessary personal data in tool results. Treat `_meta` as hidden from the model, not as a substitute for authorization or secure storage. ## Import skills from the MCP server Configure the MCP server to supply skills when you want to version and deploy their instructions and supporting files with the server. During plugin submission, **Scan Tools** imports a static snapshot of those skills into the draft. OpenAI currently supports a bounded, static subset of the [draft SEP-2640 Skills extension](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/2640). This proposal is not yet part of the stable MCP specification. ### Advertise the extension Declare `io.modelcontextprotocol/skills` in the server’s initialization capabilities: ``` { "capabilities": { "extensions": { "io.modelcontextprotocol/skills": {} } } } ``` The declaration must be under `capabilities.extensions`. OpenAI does not recognize the earlier `experimental` declaration. ### List the skills and their resources Support the paginated `skills/list` method. Each entry must include: * A `uri` that points to the skill’s `SKILL.md`. * `frontmatter` containing every entry from the parsed `SKILL.md` front matter. Include the `name` and `description` entries. * A complete `resources` list containing `SKILL.md` and every supporting file. * A SHA-256 digest for each resource in the form `sha256:<64 lowercase hexadecimal characters>`. Use the `skill://` URI convention. The directory containing `SKILL.md` must match the skill name. For example: ``` { "skills": [ { "uri": "skill://dice-roller/tabletop-dice/SKILL.md", "frontmatter": { "name": "tabletop-dice", "description": "Roll one or more dice and report each result and the total." }, "resources": [ { "uri": "skill://dice-roller/tabletop-dice/SKILL.md", "digest": "sha256:0123456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef" }, { "uri": "skill://dice-roller/tabletop-dice/references/notation.md", "digest": "sha256:abcdef0123456789abcdef0123456789abcdef0123456789abcdef0123456789" } ] } ], "nextCursor": "optional-next-page-cursor" } ``` The example digests show the required format. For a text resource, hash the UTF-8 bytes of `content.text`. For a blob resource, base64-decode `content.blob`, then hash the decoded bytes. Also support `skills/get` for each listed `SKILL.md` URI. Return a `skill` object with the same complete entry shape as `skills/list`. Use these request parameters: * For the first `skills/list` request, accept an empty object (`{}`). * For each later `skills/list` request, accept the returned cursor, such as `{ "cursor": "next-page-cursor" }`. * For `skills/get`, accept the catalog URI, such as `{ "uri": "skill://dice-roller/tabletop-dice/SKILL.md" }`. ### Return every listed resource Support `resources/read` for every URI in the manifest. Return exactly one content item whose URI matches the request. OpenAI accepts UTF-8 text or a base64-encoded blob. During import, OpenAI verifies that: * OpenAI can fetch every listed resource and confirm its digest. * The fetched `SKILL.md` front matter exactly matches the catalog entry. * Resource paths are safe, unique, and free of normalization conflicts. * The complete skill fits the import limits. The importer accepts up to five uniquely named skills across 10 catalog pages. Each skill can contain up to 100 files, with these size limits: | Content | Limit | | --- | --- | | `SKILL.md` | 256 KiB | | Each supporting file | 1 MiB | | All resources for one skill | 5 MiB | | Generated skill archives for one scan | 8 MiB | The combined archive limit includes ZIP packaging overhead. If any entry fails validation or exceeds a limit, **Scan Tools** still returns the server’s tools but does not update the draft’s imported skills. Fix the server and scan again. Skills imported from MCP are submission-time snapshots, not live runtime resources. After changing a skill, run **Scan Tools** again, review the imported skills, and submit a new plugin version. See [Submit plugins](https://developers.openai.com/plugins/deploy/submission#mcp) for the complete flow. ## Authenticate and authorize requests Add authentication when a tool reads private data or takes action for a user. Enforce authorization in the MCP server for every request; never rely on the model to decide whether a user has access. See [Authenticate users](https://developers.openai.com/plugins/build/auth) for OAuth discovery, security schemes, and authorization challenges. ## Tool annotations and elicitation Set annotations according to actual behavior: * `readOnlyHint`: `true` only when the tool cannot change state. * `destructiveHint`: `true` when a tool can cause irreversible or difficult to reverse outcomes. * `openWorldHint`: `true` when a tool can affect public or external systems. Annotations help ChatGPT and Codex choose appropriate confirmation and safety behavior. They do not replace authorization, validation, or confirmation in your server. Use MCP elicitation when the server needs structured information that was not provided in the original tool call. Keep elicitation focused on information the user can reasonably supply. Do not use it to collect secrets or bypass normal authentication. ## Company knowledge compatibility Company knowledge can use read-only tools from your MCP server. To make a plugin eligible as a company knowledge source, implement the standard `search` and `fetch` tool input schemas and mark other read-only tools with `readOnlyHint: true`. Return absolute, user-openable URLs for sources that the model should cite. Keep internal document identifiers in the result’s `id` field. For the required schemas and result shapes, see [Building MCP servers for ChatGPT and API integrations](https://platform.openai.com/docs/mcp). ## Run and test locally Expose a streamable HTTP endpoint, typically at `/mcp`, then inspect it with [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector): `npx @modelcontextprotocol/inspector` In the Inspector UI, select **Streamable HTTP** and enter `http://localhost:3000/mcp`. Use the inspector to: 1. Confirm that initialization succeeds. 2. Review server instructions and the advertised tool list. 3. Call every tool with representative and invalid inputs. 4. Verify schemas, results, errors, and annotations. 5. Confirm that authorization is enforced for private data and write actions. Then connect the server to ChatGPT in [developer mode](https://developers.openai.com/plugins/deploy/connect-chatgpt) and run the direct, indirect, edge-case, and out-of-scope requests from your use-case inventory. ## Deploy the endpoint For public plugin submission, deploy the MCP server at a stable, publicly reachable HTTPS endpoint. [Secure MCP Tunnel](https://developers.openai.com/api/docs/guides/secure-mcp-tunnels) can connect a private MCP server in developer mode, but it does not satisfy public submission requirements. The production endpoint must: * Support the MCP streamable HTTP transport. * Respond at a stable URL, typically ending in `/mcp`. * Meet the latency and availability needs of the plugin’s workflows. * Reach required services and data stores. * Preserve authentication and authorization boundaries. * Produce logs and metrics for failed initialization and tool calls. If the MCP server must remain private, deploy a public HTTPS proxy that forwards MCP requests to the private server. Use [OpenAI-managed mTLS](https://developers.openai.com/plugins/build/auth#mutual-tls-mtls) to authenticate ChatGPT as the MCP client, and use [OAuth 2.1](https://developers.openai.com/plugins/build/auth) when your plugin requires user authentication. If your network requires an IP allowlist, use the published [ChatGPT connectors IP ranges](https://developers.openai.com/api/docs/guides/ip-addresses) and update the allowlist automatically. An IP allowlist does not replace authentication or authorization. The public endpoint must remain reachable for plugin review and [domain verification](https://developers.openai.com/plugins/deploy/submission#domain-verification). Do not use Secure MCP Tunnel alone, a temporary tunnel, or a local endpoint for public submission. ### Choose infrastructure You can deploy the MCP server to serverless, container, edge, or traditional application infrastructure. Choose a platform based on: * Runtime and dependency support. * Streaming response behavior. * Cold-start and request latency. * Network access to required services. * Data residency and compliance requirements. * Secret management. * Logging, tracing, and alerting. * Rollback and versioning support. If the server also hosts optional UI assets, deploy those assets at stable origins allowed by the component’s [content security policy](https://developers.openai.com/plugins/build/chatgpt-ui#content-security-policy-csp). ### Configure the production endpoint Before deployment: 1. Set production credentials through the host’s secret-management system. 2. Configure the authorization server and allowed redirect behavior. 3. Apply timeouts and rate limits to expensive or externally visible tools. 4. Remove debug responses and unnecessary personal data. 5. Confirm that logs do not contain access tokens or sensitive tool results. After deployment, call the production endpoint with [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector). Verify initialization, server instructions, tools, schemas, annotations, authentication, results, and errors. ### Plan for updates Keep published tool names and schemas backward compatible. Add fields or tools without breaking existing contracts. If metadata changes, refresh the developer-mode connection and rerun the evaluation set before submission. For optional UI, version resource identifiers when HTML, JavaScript, or CSS changes in a way that could break a cached component. ## Add optional UI After tools work end to end, decide whether any use case needs visual interaction. A table, map, editable schedule, or comparison view may benefit from UI. A lookup, status check, or background action often does not. Continue with [Add UI to your MCP server](https://developers.openai.com/plugins/build/chatgpt-ui) to register an MCP Apps resource and associate it with selected tools. ## Security reminders * Treat every tool input as untrusted. * Validate parameters and enforce authorization on the server. * Require confirmation for consequential write actions. * Keep secrets and sensitive data out of tool metadata and results. * Log enough context to investigate failures without logging credentials or unnecessary personal data. * Rate-limit expensive or externally visible actions. 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