[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-tanstack-ai-mcp":3,"mdc--x5u8gk-key":53,"related-repo-tanstack-ai-mcp":13029,"related-org-tanstack-ai-mcp":13131},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":48,"sourceUrl":51,"mdContent":52},"ai-mcp","connect and run MCP tools","Host-side Model Context Protocol (MCP) client for TanStack AI: connect to external MCP servers, discover and run their tools inside any adapter's chat() loop, read resources and prompts, generate TypeScript types (typed tool names\u002Fpool keys) with the bundled CLI, and manage lifecycle with close()\u002Fawait using.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"tanstack","TanStack","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Ftanstack.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"MCP","mcp","tag",{"name":17,"slug":18,"type":15},"Agents","agents",{"name":20,"slug":21,"type":15},"AI Infrastructure","ai-infrastructure",{"name":9,"slug":8,"type":15},2884,"https:\u002F\u002Fgithub.com\u002FTanStack\u002Fai","2026-07-16T06:04:30.739067",null,269,[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,8,44,45,46,47],"ai","ai-agents","ai-sdk","anthropic","chatbot","function-calling","gemini","generative-ai","llm","multimodal","openai","react","solidjs","streaming","svelte","tool-calling","typescript","typescript-sdk","vue",{"repoUrl":24,"stars":23,"forks":27,"topics":49,"description":50},[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,8,44,45,46,47],"šŸ¤– Type-safe, provider-agnostic TypeScript AI SDK for streaming chat, tool calling, agents, and multimodal apps across OpenAI, Anthropic, Gemini, React, Vue, Svelte, and Solid.","https:\u002F\u002Fgithub.com\u002FTanStack\u002Fai\u002Ftree\u002FHEAD\u002Fpackages\u002Fai-mcp\u002Fskills\u002Fai-mcp","---\nname: ai-mcp\ndescription: >\n Host-side Model Context Protocol (MCP) client for TanStack AI: connect to\n external MCP servers, discover and run their tools inside any adapter's\n chat() loop, read resources and prompts, generate TypeScript types (typed tool names\u002Fpool keys)\n with the bundled CLI, and manage lifecycle with close()\u002Fawait using.\ntype: sub-skill\nlibrary: tanstack-ai\nlibrary_version: '0.10.0'\nsources:\n - 'TanStack\u002Fai:docs\u002Ftools\u002Fmcp.md'\n - 'TanStack\u002Fai:packages\u002Fai-mcp\u002Fsrc\u002Fclient.ts'\n - 'TanStack\u002Fai:packages\u002Fai-mcp\u002Fsrc\u002Fpool.ts'\n - 'TanStack\u002Fai:packages\u002Fai-mcp\u002Fsrc\u002Fresources.ts'\n - 'TanStack\u002Fai:packages\u002Fai-mcp\u002Fsrc\u002Ftransport.ts'\n---\n\n# `@tanstack\u002Fai-mcp`\n\nThis skill covers the `@tanstack\u002Fai-mcp` package. Read `ai-core\u002Ftool-calling\u002FSKILL.md`\nfirst ā€” MCP tools flow into `chat()` the same way hand-written tools do.\n\n## When to use this package\n\nUse `@tanstack\u002Fai-mcp` when:\n\n- A third-party MCP server exposes tools you want an agent or chat loop to call.\n- You want to read MCP server resources (files, text, data) or prompts into a\n `chat()` message list.\n- You want generated TypeScript types for an external MCP server's tool\n signatures (via the bundled `generate` CLI).\n- You are running tool execution on the server side and want to connect to MCP\n servers with HTTP (Streamable HTTP or SSE) or stdio transports.\n\nDo NOT use this package for browser\u002Fclient-side code ā€” MCP connections are\nserver-side only.\n\n## Install\n\n```bash\npnpm add @tanstack\u002Fai-mcp\n```\n\nThe package has two subpath exports:\n\n- `.` ā€” main client API (`createMCPClient`, `createMCPClients`, converters, types)\n- `.\u002Fstdio` ā€” Node-only stdio transport factory (`stdioTransport`); import it\n separately so edge bundles stay clean\n\n## `createMCPClient` ā€” single server\n\n```typescript\nimport { createMCPClient } from '@tanstack\u002Fai-mcp'\n\nconst client = await createMCPClient({\n transport: { type: 'http', url: 'https:\u002F\u002Fmcp.example.com\u002Fmcp' },\n prefix: 'weather', \u002F\u002F optional: prefixes all tool names (e.g. 'weather_get_forecast')\n name: 'my-app', \u002F\u002F optional: client identity sent to the server\n})\n```\n\n`createMCPClient` connects immediately and returns an `MCPClient`. Throws\n`MCPConnectionError` if the connection fails.\n\n### Transports\n\n#### Streamable HTTP (default for internet-facing servers)\n\n```typescript\nconst client = await createMCPClient({\n transport: {\n type: 'http',\n url: 'https:\u002F\u002Fmcp.example.com\u002Fmcp',\n headers: { Authorization: 'Bearer sk-...' },\n },\n})\n```\n\n#### SSE\n\n```typescript\nconst client = await createMCPClient({\n transport: {\n type: 'sse',\n url: 'https:\u002F\u002Fmcp.example.com\u002Fsse',\n headers: { Authorization: 'Bearer sk-...' },\n },\n})\n```\n\n#### stdio (Node-only ā€” import from `\u002Fstdio` subpath)\n\n```typescript\nimport { createMCPClient } from '@tanstack\u002Fai-mcp'\nimport { stdioTransport } from '@tanstack\u002Fai-mcp\u002Fstdio'\n\nconst client = await createMCPClient({\n transport: stdioTransport({\n command: 'npx',\n args: ['-y', 'my-mcp-server'],\n env: { API_KEY: process.env.API_KEY ?? '' },\n }),\n})\n```\n\n#### Custom transport (escape hatch)\n\nPass any SDK `Transport` instance directly:\n\n```typescript\nimport { createMCPClient } from '@tanstack\u002Fai-mcp'\nimport { InMemoryTransport } from '@modelcontextprotocol\u002Fsdk\u002FinMemory.js'\n\nconst [clientTransport] = InMemoryTransport.createLinkedPair()\nconst client = await createMCPClient({ transport: clientTransport })\n```\n\n### Authentication\n\nTwo levels:\n\n- **Static tokens** ā€” pass `headers` on the `http`\u002F`sse` config (sent with\n every request): `headers: { Authorization: 'Bearer ...' }`.\n- **OAuth 2.1 (MCP authorization spec)** ā€” pass `authProvider` on the\n `http`\u002F`sse` config. It accepts any `OAuthClientProvider` from\n `@modelcontextprotocol\u002Fsdk\u002Fclient\u002Fauth.js`; the SDK transport attaches\n tokens, refreshes them, and retries on 401.\n\n```typescript\nimport { createMCPClient } from '@tanstack\u002Fai-mcp'\nimport type { OAuthClientProvider } from '@modelcontextprotocol\u002Fsdk\u002Fclient\u002Fauth.js'\n\ndeclare const myOAuthProvider: OAuthClientProvider \u002F\u002F backed by stored tokens\n\nconst client = await createMCPClient({\n transport: {\n type: 'http',\n url: 'https:\u002F\u002Fmcp.example.com\u002Fmcp',\n authProvider: myOAuthProvider,\n },\n})\n```\n\nCaveat: interactive authorization-code flows need `transport.finishAuth(code)`,\nand `createMCPClient` does not expose its internal transport. For redirect\nflows, construct the `StreamableHTTPClientTransport` yourself with the\n`authProvider`, keep a reference, call `finishAuth(code)` in the OAuth\ncallback route, then pass the transport via the escape hatch above. For\nserver-side providers backed by pre-provisioned\u002Frefreshable tokens, the\nconfig form is sufficient.\n\n## Three type-safety modes\n\n### Mode 1 ā€” Auto-discovery (no types needed)\n\n`client.tools()` lists every tool the server exposes. Args are typed `unknown`\nat compile time but the tool's JSON Schema is forwarded to the LLM.\n\n```typescript\nconst tools = await client.tools()\n\u002F\u002F tools: ServerTool[] (args unknown)\n\nconst stream = chat({\n adapter: openaiText('gpt-5.5'),\n messages,\n tools,\n})\n```\n\nUse `{ lazy: true }` to defer schema sending via the existing `LazyToolManager`:\n\n```typescript\nconst tools = await client.tools({ lazy: true })\n```\n\n### Mode 2 ā€” Typed via `toolDefinition` instances\n\nPass bare `toolDefinition()` instances (no `.server()` call) to `client.tools([...])`.\nThe MCP client binds a `callTool` proxy as the execute function while\ninput\u002Foutput validation and TypeScript types come from the definitions' Zod schemas.\nOnly the named tools are returned (allowlist = the definitions' `name`s).\nThrows `MCPToolNotFoundError` if the server does not expose a tool with that name.\n\n```typescript\nimport { toolDefinition } from '@tanstack\u002Fai'\nimport { createMCPClient } from '@tanstack\u002Fai-mcp'\nimport { z } from 'zod'\n\nconst getWeatherDef = toolDefinition({\n name: 'get_weather',\n description: 'Current weather for a city',\n inputSchema: z.object({ city: z.string() }),\n outputSchema: z.object({ temperature: z.number(), conditions: z.string() }),\n})\n\nconst client = await createMCPClient({\n transport: { type: 'http', url: 'https:\u002F\u002Fmcp.example.com\u002Fmcp' },\n})\n\n\u002F\u002F Returns MappedServerTools\u003Ctypeof defs> ā€” fully typed per definition.\nconst tools = await client.tools([getWeatherDef])\n```\n\n### Mode 3 ā€” Generated types (via `generate` CLI)\n\nRun `npx @tanstack\u002Fai-mcp generate` to introspect live servers and emit a\n`ServerDescriptor` interface per server. Pass the generated interface as the\ngeneric to `createMCPClient\u003CWeatherServer>(...)` to narrow discovered tool\nnames to the server's literals (args stay untyped ā€” use Mode 2 for typed args).\n\nSee the \"Codegen CLI\" section below for details.\n\n## Lifecycle\n\n**The caller owns the lifecycle.** `chat()` never closes the client.\n\nTools execute lazily while the response stream is consumed ā€” close only after\nthe stream is drained. In a streaming route handler, `try\u002Ffinally` around the\n`return` (or `await using` at function scope) closes the client before the\nbody streams; use a middleware terminal hook there instead (see Common\nMistakes below).\n\n```typescript\n\u002F\u002F Option 1: middleware terminal hooks (streaming route handlers)\nconst client = await createMCPClient({\n transport: { type: 'http', url: '...' },\n})\nconst stream = chat({\n adapter: openaiText('gpt-5.5'),\n messages,\n tools: await client.tools(),\n middleware: [\n {\n name: 'mcp-close',\n onFinish: () => client.close(),\n onAbort: () => client.close(),\n onError: () => client.close(),\n },\n ],\n})\nreturn toServerSentEventsResponse(stream)\n\n\u002F\u002F Option 2: explicit close after in-scope consumption\nconst client = await createMCPClient({\n transport: { type: 'http', url: '...' },\n})\ntry {\n const stream = chat({\n adapter: openaiText('gpt-5.5'),\n messages,\n tools: await client.tools(),\n })\n for await (const chunk of stream) {\n \u002F\u002F stream fully consumed inside this block\n }\n} finally {\n await client.close()\n}\n\n\u002F\u002F Option 3: await using (TypeScript 5.2+ with Symbol.asyncDispose) ā€”\n\u002F\u002F same rule: consume the stream before the scope exits.\nawait using client = await createMCPClient({\n transport: { type: 'http', url: '...' },\n})\n\u002F\u002F ... consume the stream in this scope; close() runs at scope exit\n```\n\n## `chat({ mcp })` ā€” discovery + lifecycle in one prop\n\nRather than calling `client.tools()` and `client.close()` yourself, pass the\n`mcp` option to `chat()` and let it manage the full lifecycle.\n\n```typescript\n\u002F\u002F ChatMCPOptions shape:\n\u002F\u002F mcp: {\n\u002F\u002F clients: Array\u003CMCPClient | MCPClients>,\n\u002F\u002F connection?: 'close' | 'keep-alive', \u002F\u002F default: 'close'\n\u002F\u002F lazyTools?: boolean,\n\u002F\u002F onDiscoveryError?: (error: unknown, source) => void,\n\u002F\u002F }\n```\n\n**Behavior:**\n\n- `chat()` calls `.tools()` on every entry in `clients` at run start and merges\n all results into the tool list.\n- `lazyTools: true` is forwarded to `tools({ lazy: true })`.\n- `connection: 'close'` (default) ā€” each client is closed when the run ends\n (after the agent loop completes and the stream is drained). With\n `'keep-alive'`, `chat()` never closes the clients ā€” the caller owns their\n lifecycle (keep connections warm across requests).\n- `onDiscoveryError`: throw (or re-throw) to abort the entire call; return\n normally to skip that source and continue. Omitting the handler re-throws\n (fail-fast).\n\n**When to use `mcp` vs. the tools spread:**\n\n| Approach | Use when |\n| ------------------------------------------------------- | ------------------------------------------------------------------------- |\n| `chat({ mcp: { clients: [...] } })` | Convenience: discovery + lifecycle handled for you; untyped args are fine |\n| `tools: [...await client.tools([toolDefinition(...)])]` | Fully-typed args\u002Fresults via Zod schemas (`toolDefinition` mode) |\n\n**Server-side example:**\n\n```typescript\nimport { createFileRoute } from '@tanstack\u002Freact-router'\nimport { chat, toServerSentEventsResponse } from '@tanstack\u002Fai'\nimport { openaiText } from '@tanstack\u002Fai-openai'\nimport { createMCPClient } from '@tanstack\u002Fai-mcp'\n\nexport const Route = createFileRoute('\u002Fapi\u002Fchat')({\n server: {\n handlers: {\n POST: async ({ request }) => {\n const { messages } = await request.json()\n\n const mcpClient = await createMCPClient({\n transport: { type: 'http', url: 'https:\u002F\u002Fmcp.example.com\u002Fmcp' },\n })\n\n const stream = chat({\n adapter: openaiText('gpt-5.5'),\n messages,\n mcp: {\n clients: [mcpClient],\n connection: 'keep-alive', \u002F\u002F chat() won't close it ā€” reuse across requests\n onDiscoveryError: (err, source) => {\n console.warn('MCP discovery failed for source, skipping:', err)\n \u002F\u002F returning skips this source; throw to fail the whole call fast\n },\n },\n })\n\n return toServerSentEventsResponse(stream)\n \u002F\u002F connection: 'keep-alive' ā€” chat() never closes mcpClient; it stays warm for the next request.\n },\n },\n },\n})\n```\n\nYou can also pass an `MCPClients` pool directly:\n\n```typescript\nconst pool = await createMCPClients({\n github: { transport: { type: 'http', url: 'https:\u002F\u002Fmcp.github.com\u002Fmcp' } },\n linear: { transport: { type: 'http', url: 'https:\u002F\u002Fmcp.linear.app\u002Fmcp' } },\n})\n\nconst stream = chat({\n adapter: openaiText('gpt-5.5'),\n messages,\n mcp: { clients: [pool], connection: 'keep-alive' },\n})\n```\n\n## `createMCPClients` ā€” multiple servers\n\nConnect to many MCP servers in parallel. Each config key becomes the default\nprefix for that server's tools, preventing name collisions across servers.\n\n```typescript\nimport { createMCPClients } from '@tanstack\u002Fai-mcp'\n\nawait using pool = await createMCPClients({\n github: { transport: { type: 'http', url: 'https:\u002F\u002Fmcp.github.com\u002Fmcp' } },\n linear: { transport: { type: 'http', url: 'https:\u002F\u002Fmcp.linear.app\u002Fmcp' } },\n})\n\n\u002F\u002F Tool names auto-prefixed: 'github_search_repos', 'linear_create_issue', etc.\nconst tools = await pool.tools()\n\n\u002F\u002F Forward lazy flag to every server:\nconst lazyTools = await pool.tools({ lazy: true })\n\n\u002F\u002F Per-server typed access:\nconst githubTools = await pool.clients.github.tools()\n```\n\n`createMCPClients` connects in parallel, closes already-connected clients if\nany connection fails (no leaks), and throws `MCPConnectionError` naming the\nfailed server(s).\n\nOverride or disable prefixing:\n\n```typescript\nawait using pool = await createMCPClients({\n github: { transport: { ... }, prefix: 'gh' }, \u002F\u002F 'gh_search_repos'\n linear: { transport: { ... }, prefix: '' }, \u002F\u002F 'create_issue' (no prefix)\n})\n```\n\n## Abort signal ā€” cancelling in-flight MCP calls\n\nMCP tool calls are automatically cancelled when the chat run's `AbortController`\nfires (e.g. client disconnect, server abort). The `abortSignal` is threaded\nthrough `ToolExecutionContext` into every `callTool` call with no extra code.\n\nYou can also read it in a hand-written server tool that wraps an MCP call:\n\n```typescript\nconst myTool = myDef.server(async (args, ctx) => {\n \u002F\u002F Forward to any async work that accepts an AbortSignal.\n const result = await fetch('https:\u002F\u002Fslow.api\u002Fdata', {\n signal: ctx?.abortSignal,\n })\n return result.json()\n})\n```\n\n## Resources\n\n```typescript\n\u002F\u002F List all resources the server exposes.\nconst resources = await client.resources()\n\n\u002F\u002F Read a specific resource by URI.\nconst resource = await client.readResource(resources[0].uri)\n\n\u002F\u002F Convert one content block to a TanStack ContentPart.\nimport { mcpResourceToContentPart } from '@tanstack\u002Fai-mcp'\n\nconst part = mcpResourceToContentPart(resource.contents[0])\n\u002F\u002F part: ContentPart (type: 'text' always for v1)\n```\n\nInject resources into a chat turn:\n\n```typescript\nimport { chat } from '@tanstack\u002Fai'\nimport { createMCPClient, mcpResourceToContentPart } from '@tanstack\u002Fai-mcp'\n\nconst client = await createMCPClient({\n transport: { type: 'http', url: '...' },\n})\nconst resource = await client.readResource('file:\u002F\u002F\u002Fproject\u002FREADME.md')\nconst parts = resource.contents.map(mcpResourceToContentPart)\n\nconst stream = chat({\n adapter: openaiText('gpt-5.5'),\n messages: [\n {\n role: 'user',\n content: [\n ...parts,\n { type: 'text', content: 'Summarize this document.' },\n ],\n },\n ],\n})\n```\n\n## Prompts\n\n```typescript\n\u002F\u002F List prompts the server exposes.\nconst prompts = await client.prompts()\n\n\u002F\u002F Get a prompt (with optional arguments).\nconst prompt = await client.getPrompt('review_code', { language: 'TypeScript' })\n\n\u002F\u002F Convert to TanStack ModelMessage[] for use in chat().\nimport { mcpPromptToMessages } from '@tanstack\u002Fai-mcp'\n\nconst messages = mcpPromptToMessages(prompt)\n\u002F\u002F messages: ModelMessage[] (role: 'user' | 'assistant')\n\nconst stream = chat({\n adapter: openaiText('gpt-5.5'),\n messages: [...messages, ...userMessages],\n})\n```\n\n## MCP Apps\n\nMCP Apps let an MCP tool surface a UI widget (static or interactive) on the\nclient. Two variants exist. See `docs\u002Fmcp\u002Fapps.md` for the full guide.\n\n### Static widgets ā€” `UIResourcePart`\n\nWhen an MCP tool result carries a `ui:\u002F\u002F` resource, TanStack AI emits a\n`UIResourcePart` on the **assistant `UIMessage`**, alongside the normal\n`ToolCallPart` \u002F `ToolResultPart`. It is purely presentational ā€” it never\nenters model input. The resource is read eagerly during the `chat()` run; if\nthe read fails the tool result still flows to the model and the widget is\nsimply absent (fail-soft). Static widgets require the MCP source to expose\n`readResource` ā€” both `createMCPClient` and a `createMCPClients` pool do.\n\n```typescript\nimport type { UIResourcePart } from '@tanstack\u002Fai'\n\n\u002F\u002F UIResourcePart shape (on the assistant UIMessage):\n\u002F\u002F {\n\u002F\u002F type: 'ui-resource'\n\u002F\u002F resource: { uri: string; mimeType: string; text?: string; blob?: string }\n\u002F\u002F serverId?: string \u002F\u002F pool prefix \u002F config key ā€” routes interactive calls\n\u002F\u002F toolCallId: string \u002F\u002F links to the originating tool call\n\u002F\u002F toolName: string \u002F\u002F server-native MCP tool name whose UI this renders\n\u002F\u002F meta?: Record\u003Cstring, unknown> \u002F\u002F reserved ā€” currently always undefined\n\u002F\u002F }\n```\n\n### Interactive apps ā€” `createMcpAppCallHandler`\n\nFor interactive apps (the widget iframe posts tool-call \u002F prompt \u002F link\nactions back), mount `createMcpAppCallHandler` from `@tanstack\u002Fai-mcp\u002Fapps`\nat a POST route. Pass the MCP client(s) you already created ā€” a single\n`MCPClient`, an `MCPClients` pool, or an array of either. The handler reads\neach client's transport descriptor via `client.getInfo()` \u002F\n`pool.getServers()` (pure config, not a live socket) and **reconnects\nper-call** (stateless \u002F serverless-safe). It matches the widget-supplied\nnative (unprefixed) tool name against the server's unprefixed tool names,\nenforces a same-server allowlist, and returns `{ ok: true, result }` or\n`{ ok: false, error }`.\n\nFor a pool, the `serverId` on the `UIResourcePart` is the config key (the\ntool prefix); for a single client it is the client's `prefix` (or the sole\ndefault when `serverId` is absent and there is exactly one client).\n\n```typescript\nimport { createMCPClients } from '@tanstack\u002Fai-mcp'\nimport {\n createMcpAppCallHandler,\n inMemoryMcpSessionStore,\n} from '@tanstack\u002Fai-mcp\u002Fapps'\n\n\u002F\u002F Reuse the same pool you pass to chat({ mcp: { clients: [mcp] } }).\nconst mcp = await createMCPClients({\n weather: {\n transport: { type: 'http', url: 'https:\u002F\u002Fmcp-app.example.com\u002Fmcp' },\n },\n})\n\n\u002F\u002F Minimal ā€” reconnect-per-call via getServers() descriptor.\nconst handler = createMcpAppCallHandler({ clients: mcp })\n\n\u002F\u002F Options:\n\u002F\u002F clients ā€” MCPClient | MCPClients | Array\u003CMCPClient | MCPClients> (required).\n\u002F\u002F The handler reads transport descriptors via client.getInfo() \u002F\n\u002F\u002F pool.getServers() ā€” the client does not need a live connection.\n\u002F\u002F store ā€” optional dynamic\u002Fstateful session store (e.g.\n\u002F\u002F inMemoryMcpSessionStore()); used alongside clients.\n\u002F\u002F allowTool ā€” optional authorizer receiving the WHOLE request:\n\u002F\u002F (req: McpAppCallRequest) => boolean | Promise\u003Cboolean>.\n\u002F\u002F The server-exposure check is ALWAYS enforced (the handler\n\u002F\u002F rejects any tool the server does not expose). `allowTool`\n\u002F\u002F is an ADDITIONAL restriction AND-ed on top: a request must\n\u002F\u002F satisfy BOTH the server-exposure check and allowTool.\nconst handlerWithStore = createMcpAppCallHandler({\n clients: mcp,\n store: inMemoryMcpSessionStore(),\n allowTool: (req) => req.toolName === 'place_order',\n})\n```\n\nThe handler invokes the server (`body: { threadId, serverId?, toolName, args?, messageId? }`):\n\n```typescript\nconst result = await handler(body)\n\u002F\u002F { ok: true; result: unknown } | { ok: false; error: string }\n```\n\n### Client side ā€” `useMcpAppBridge` + `MCPAppResource`\n\nIn React\u002FPreact, create the bridge with the `useMcpAppBridge` hook (from\n`@tanstack\u002Fai-react` \u002F `@tanstack\u002Fai-preact`) ā€” it returns a **stable** bridge\nper `threadId`\u002F`callEndpoint` and always calls your latest `sendMessage`\u002F`onLink`,\nso the bridge isn't recreated on every render (no `useMemo` \u002F `exhaustive-deps`\nby hand). It's a thin wrapper over the framework-agnostic `createMcpAppBridge`\nfrom `@tanstack\u002Fai-client` (use that directly outside React\u002FPreact). Render\nresources with `MCPAppResource` from `@tanstack\u002Fai-react\u002Fmcp-apps` (also\n`@tanstack\u002Fai-preact\u002Fmcp-apps`, which requires a `preact\u002Fcompat` alias).\n`MCPAppResource` uses `@mcp-ui\u002Fclient`'s `AppRenderer` under the hood ā€” React\nonly. Solid, Vue, Svelte, and Angular renderers are deferred.\n\nThe bridge exposes `{ callTool, sendPrompt, openLink }` and routes the\niframe's actions: `tool` ā†’ POST to `callEndpoint`; `prompt` ā†’\n`chat.sendMessage`; `link` ā†’ `onLink(url)` if provided, otherwise the link\nis dropped (with a console warning) and `openLink` returns `{ isError: true }`\nā€” it does NOT hang. `toolName` is read from `part.toolName`; it is not a\nprop. Omit `bridge` for display-only (inert) rendering.\n\n```tsx\nimport { useChat, useMcpAppBridge } from '@tanstack\u002Fai-react'\nimport { fetchServerSentEvents } from '@tanstack\u002Fai-client'\nimport { MCPAppResource } from '@tanstack\u002Fai-react\u002Fmcp-apps'\n\nfunction ChatPage() {\n const threadId = 'weather-chat'\n const { messages, sendMessage } = useChat({\n connection: fetchServerSentEvents('\u002Fapi\u002Fchat'),\n })\n\n const bridge = useMcpAppBridge({\n threadId,\n callEndpoint: '\u002Fapi\u002Fmcp-app\u002Fcall',\n chat: { sendMessage: async (content) => void sendMessage({ content }) },\n \u002F\u002F Opt in to link navigation ā€” absent means links are dropped.\n onLink: (url) => window.open(url, '_blank', 'noopener'),\n })\n\n return (\n \u003Cdiv>\n {messages.map((msg) =>\n msg.parts.map((part, i) => {\n if (part.type === 'text') return \u003Cp key={i}>{part.content}\u003C\u002Fp>\n if (part.type === 'ui-resource') {\n return (\n \u003CMCPAppResource\n key={i}\n part={part}\n bridge={bridge}\n sandbox={{ url: new URL('https:\u002F\u002Fsandbox.example.com') }}\n \u002F\u002F toolInput is optional; toolName comes from part.toolName.\n \u002F>\n )\n }\n return null\n }),\n )}\n \u003C\u002Fdiv>\n )\n}\n```\n\n## Codegen CLI\n\nGenerate TypeScript types (typed tool names and pool keys) by introspecting live MCP servers.\n\n**1. Create `mcp.config.ts` at your project root:**\n\n```typescript\nimport { defineConfig } from '@tanstack\u002Fai-mcp'\n\nexport default defineConfig({\n servers: {\n github: {\n transport: { type: 'http', url: 'https:\u002F\u002Fmcp.github.com\u002Fmcp' },\n \u002F\u002F prefix must match the runtime createMCPClient({ prefix }) value\n },\n },\n outFile: '.\u002Fsrc\u002Fmcp-types.generated.ts',\n})\n```\n\n**2. Run the generator:**\n\n```bash\nnpx @tanstack\u002Fai-mcp generate\n```\n\nThis connects to each server, lists its tools\u002Fresources\u002Fprompts, converts JSON\nSchemas to TypeScript, and writes one `interface \u003CName>Server extends ServerDescriptor`\nper server plus a combined `interface MCPServers` for pool typing.\n\n**3. Use the generated types:**\n\n```typescript\n\u002F\u002F Single server ā€” narrows tools() return to descriptor-keyed tool names.\nimport type { GithubServer } from '.\u002Fsrc\u002Fmcp-types.generated'\nimport { createMCPClient } from '@tanstack\u002Fai-mcp'\n\nconst client = await createMCPClient\u003CGithubServer>({\n transport: { type: 'http', url: 'https:\u002F\u002Fmcp.github.com\u002Fmcp' },\n})\nconst tools = await client.tools() \u002F\u002F typed to GithubServer's tool names\n\n\u002F\u002F Multiple servers via the generated MCPServers map.\nimport type { MCPServers } from '.\u002Fsrc\u002Fmcp-types.generated'\n\nconst pool = await createMCPClients\u003CMCPServers>({\n github: { transport: { type: 'http', url: 'https:\u002F\u002Fmcp.github.com\u002Fmcp' } },\n})\n\u002F\u002F pool.clients.github is MCPClient\u003CGithubServer>\n\u002F\u002F missing\u002Fextra keys are a compile error\n```\n\nCodegen deps (`json-schema-to-typescript`, `jiti`) are bundled into the CLI bin\nand do NOT appear in the library's runtime dependency graph.\n\n## Error classes\n\n- `MCPConnectionError` ā€” thrown when a server connection fails or when calling\n methods after `close()`.\n- `MCPToolNotFoundError` ā€” thrown from `client.tools([defs])` when a definition's\n `name` is not exposed by the server.\n- `MCPTaskRequiredToolError` ā€” thrown from `client.tools([defs])` when the named\n tool declares `execution.taskSupport: 'required'` (experimental MCP tasks).\n Such tools only run via the SDK's `tasks\u002FcallToolStream` flow, which\n `@tanstack\u002Fai-mcp` does not support yet; they are silently excluded from\n `tools()` auto-discovery for the same reason.\n- `DuplicateToolNameError` ā€” thrown by a single pool's own `tools()` when two\n tools within that pool share the same name (same server or pool clients with no\n prefix). Exported from `@tanstack\u002Fai-mcp`.\n- `MCPDuplicateToolNameError` ā€” thrown by `chat()` when tools from separate\n `mcp.clients` entries collide after merging. Exported from `@tanstack\u002Fai`\n (not `@tanstack\u002Fai-mcp`), so users can `instanceof` it at the `chat()` call site.\n\n```typescript\nimport {\n MCPConnectionError,\n MCPToolNotFoundError,\n MCPTaskRequiredToolError,\n DuplicateToolNameError,\n} from '@tanstack\u002Fai-mcp'\n\nimport { MCPDuplicateToolNameError } from '@tanstack\u002Fai'\n```\n\n## Complete server-route example\n\n```typescript\n\u002F\u002F src\u002Froutes\u002Fapi.chat.ts\nimport { createFileRoute } from '@tanstack\u002Freact-router'\nimport { chat, toServerSentEventsResponse } from '@tanstack\u002Fai'\nimport { openaiText } from '@tanstack\u002Fai-openai'\nimport { createMCPClients } from '@tanstack\u002Fai-mcp'\n\nexport const Route = createFileRoute('\u002Fapi\u002Fchat')({\n server: {\n handlers: {\n POST: async ({ request }) => {\n const { messages } = await request.json()\n\n const pool = await createMCPClients({\n github: {\n transport: { type: 'http', url: 'https:\u002F\u002Fmcp.github.com\u002Fmcp' },\n },\n linear: {\n transport: {\n type: 'http',\n url: 'https:\u002F\u002Fmcp.linear.app\u002Fmcp',\n headers: {\n Authorization: `Bearer ${process.env.LINEAR_KEY ?? ''}`,\n },\n },\n },\n })\n\n const stream = chat({\n adapter: openaiText('gpt-5.5'),\n messages,\n tools: await pool.tools(),\n \u002F\u002F Close after the run ends ā€” tools execute while the response streams,\n \u002F\u002F so `await using` \u002F try-finally would close the pool too early here.\n middleware: [\n {\n name: 'mcp-close',\n onFinish: () => pool.close(),\n onAbort: () => pool.close(),\n onError: () => pool.close(),\n },\n ],\n })\n\n return toServerSentEventsResponse(stream)\n },\n },\n },\n})\n```\n\n## Common Mistakes\n\n### a. HIGH: closing the client before the stream finishes\n\n`chat()` executes tools lazily as the model calls them during streaming.\nIf you close the MCP client before the response stream is fully consumed,\nin-flight tool calls will fail.\n\nWrong:\n\n```typescript\nconst tools = await client.tools()\nconst stream = chat({ adapter, messages, tools })\nawait client.close() \u002F\u002F closes before the stream runs tools\nreturn toServerSentEventsResponse(stream)\n```\n\nThis includes `try\u002Ffinally` around the `return`, and `await using` at function\nscope ā€” both close before the returned `Response` body streams.\n\nCorrect ā€” close in middleware terminal hooks (exactly one of\n`onFinish`\u002F`onAbort`\u002F`onError` fires per run), or consume the stream in scope\nbefore closing:\n\n```typescript\nimport { chat, toServerSentEventsResponse } from '@tanstack\u002Fai'\nimport { createMCPClient } from '@tanstack\u002Fai-mcp'\n\nconst client = await createMCPClient({\n transport: { type: 'http', url: '...' },\n})\n\nconst stream = chat({\n adapter: openaiText('gpt-5.5'),\n messages,\n tools: await client.tools(),\n middleware: [\n {\n name: 'mcp-close',\n onFinish: () => client.close(),\n onAbort: () => client.close(),\n onError: () => client.close(),\n },\n ],\n})\nreturn toServerSentEventsResponse(stream)\n```\n\n### b. HIGH: importing `stdioTransport` from the main entry point\n\n`stdioTransport` is only available from `@tanstack\u002Fai-mcp\u002Fstdio`. Importing it\nfrom `@tanstack\u002Fai-mcp` will fail with a module-not-found error and would\nbundle Node.js child-process code into edge bundles.\n\nWrong:\n\n```typescript\nimport { stdioTransport } from '@tanstack\u002Fai-mcp' \u002F\u002F does not exist here\n```\n\nCorrect:\n\n```typescript\nimport { stdioTransport } from '@tanstack\u002Fai-mcp\u002Fstdio'\n```\n\n### c. MEDIUM: using `client.tools([defs])` without matching names\n\nThe name field on each `toolDefinition` must exactly match the tool name the MCP\nserver exposes. Mismatches throw `MCPToolNotFoundError` at call time, not at\ntype-check time (unless generated types are in use).\n\n### d. 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all tool\npatterns (approval, lazy, client-side) apply.",{"type":68,"tag":134,"props":13013,"children":13014},{},[13015,13017,13022],{"type":79,"value":13016},"See also: ai-core\u002Fchat-experience\u002FSKILL.md ā€” wiring tools into ",{"type":68,"tag":74,"props":13018,"children":13020},{"className":13019},[],[13021],{"type":79,"value":107},{"type":79,"value":230},{"type":68,"tag":13024,"props":13025,"children":13026},"style",{},[13027],{"type":79,"value":13028},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":13030,"total":3548},[13031,13048,13061,13076,13089,13103,13117],{"slug":13032,"name":13032,"fn":13033,"description":13034,"org":13035,"tags":13036,"stars":23,"repoUrl":24,"updatedAt":13047},"ai-code-mode","execute sandboxed TypeScript code with LLMs","LLM-generated TypeScript execution in sandboxed environments: createCodeModeTool() with isolate drivers (createNodeIsolateDriver, createQuickJSIsolateDriver, createCloudflareIsolateDriver), codeModeWithSkills() for persistent skill libraries, trust strategies, skill storage (FileSystem, LocalStorage, InMemory, Mongo), client-side execution progress via code_mode:* custom events in useChat.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13037,13039,13042,13045,13046],{"name":13038,"slug":31,"type":15},"AI SDK",{"name":13040,"slug":13041,"type":15},"Code Execution","code-execution",{"name":13043,"slug":13044,"type":15},"Sandboxing","sandboxing",{"name":9,"slug":8,"type":15},{"name":7203,"slug":45,"type":15},"2026-07-16T06:04:13.597905",{"slug":13049,"name":13049,"fn":13050,"description":13051,"org":13052,"tags":13053,"stars":23,"repoUrl":24,"updatedAt":13060},"ai-core","configure TanStack AI agent features","Entry point for TanStack AI skills. Routes to chat-experience, tool-calling, media-generation, structured-outputs, adapter-configuration, ag-ui-protocol, middleware, locks, custom-backend-integration, and debug-logging, plus the skills shipped by companion packages (@tanstack\u002Fai-persistence, @tanstack\u002Fai-code-mode). Use chat() not streamText(), openaiText() not createOpenAI(), toServerSentEventsResponse() not manual SSE, middleware hooks not onEnd callbacks.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13054,13055,13057],{"name":17,"slug":18,"type":15},{"name":13056,"slug":29,"type":15},"AI",{"name":13058,"slug":13059,"type":15},"Middleware","middleware","2026-07-30T05:26:10.404565",{"slug":13062,"name":13063,"fn":13064,"description":13065,"org":13066,"tags":13067,"stars":23,"repoUrl":24,"updatedAt":13075},"ai-coreadapter-configuration","ai-core\u002Fadapter-configuration","configure AI provider adapters","Provider adapter selection and configuration: openaiText, anthropicText, geminiText, ollamaText, grokText, groqText, openRouterText, bedrockText, openaiCompatible. Per-model type safety with modelOptions, reasoning\u002Fthinking configuration, runtime adapter switching, extendAdapter() for custom models, createModel(). Generic OpenAI-compatible providers (DeepSeek, Together, Fireworks, etc.) via openaiCompatible({ baseURL, apiKey, models }) from @tanstack\u002Fai-openai\u002Fcompatible. API key env vars: OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY\u002FGEMINI_API_KEY, XAI_API_KEY, GROQ_API_KEY, OPENROUTER_API_KEY, OLLAMA_HOST, BEDROCK_API_KEY (or AWS_BEARER_TOKEN_BEDROCK).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13068,13069,13072,13074],{"name":13038,"slug":31,"type":15},{"name":13070,"slug":13071,"type":15},"Configuration","configuration",{"name":13073,"slug":37,"type":15},"LLM",{"name":9,"slug":8,"type":15},"2026-07-16T06:04:17.82075",{"slug":13077,"name":13078,"fn":13079,"description":13080,"org":13081,"tags":13082,"stars":23,"repoUrl":24,"updatedAt":13088},"ai-coreag-ui-protocol","ai-core\u002Fag-ui-protocol","implement TanStack AI streaming protocol","Server-side AG-UI streaming protocol implementation: StreamChunk event types (RUN_STARTED, TEXT_MESSAGE_START\u002FCONTENT\u002FEND, TOOL_CALL_START\u002FARGS\u002FEND, RUN_FINISHED, RUN_ERROR, STEP_STARTED\u002FSTEP_FINISHED, STATE_SNAPSHOT\u002FDELTA, CUSTOM), toServerSentEventsStream() for SSE format, toHttpStream() for NDJSON format. For backends serving AG-UI events without client packages.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13083,13086,13087],{"name":13084,"slug":13085,"type":15},"API Development","api-development",{"name":13073,"slug":37,"type":15},{"name":9,"slug":8,"type":15},"2026-07-16T06:04:10.093367",{"slug":13090,"name":13091,"fn":13092,"description":13093,"org":13094,"tags":13095,"stars":23,"repoUrl":24,"updatedAt":13102},"ai-corechat-experience","ai-core\u002Fchat-experience","implement chat experiences with TanStack AI","End-to-end chat implementation: server endpoint with chat() and toServerSentEventsResponse(), client-side useChat hook with fetchServerSentEvents(), message rendering with UIMessage parts, multimodal content, thinking\u002Freasoning display. Covers streaming states, connection adapters, and message format conversions. NOT Vercel AI SDK ā€” uses chat() not streamText().\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13096,13097,13100,13101],{"name":13084,"slug":13085,"type":15},{"name":13098,"slug":13099,"type":15},"Frontend","frontend",{"name":13073,"slug":37,"type":15},{"name":9,"slug":8,"type":15},"2026-07-30T05:26:11.505241",{"slug":13104,"name":13105,"fn":13106,"description":13107,"org":13108,"tags":13109,"stars":23,"repoUrl":24,"updatedAt":13116},"ai-coreclient-persistence","ai-core\u002Fclient-persistence","implement browser chat persistence for TanStack AI","Browser chat persistence on useChat \u002F ChatClient: localStoragePersistence, sessionStoragePersistence, indexedDBPersistence. Client-authoritative (adapter, full transcript) vs server-authoritative (persistence: true, no client cache). Reload restore, pending interrupts, mid-stream rejoin with delivery durability. Use for SPA reload durability ā€” NOT server history alone. No extra package: the adapters ship in the framework packages.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13110,13111,13112,13115],{"name":13056,"slug":29,"type":15},{"name":13098,"slug":13099,"type":15},{"name":13113,"slug":13114,"type":15},"Persistence","persistence",{"name":9,"slug":8,"type":15},"2026-07-30T05:53:35.503176",{"slug":13118,"name":13119,"fn":13120,"description":13121,"org":13122,"tags":13123,"stars":23,"repoUrl":24,"updatedAt":13130},"ai-corecustom-backend-integration","ai-core\u002Fcustom-backend-integration","integrate custom backends with TanStack AI","Connect useChat to a non-TanStack-AI backend through custom connection adapters. ConnectConnectionAdapter (single async iterable) vs SubscribeConnectionAdapter (separate subscribe\u002Fsend). Customize fetchServerSentEvents() and fetchHttpStream() with auth headers, custom URLs, and request options. Import from framework package, not @tanstack\u002Fai-client.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13124,13125,13126,13129],{"name":13056,"slug":29,"type":15},{"name":13084,"slug":13085,"type":15},{"name":13127,"slug":13128,"type":15},"Backend","backend",{"name":9,"slug":8,"type":15},"2026-07-17T06:06:39.855351",{"items":13132,"total":13272},[13133,13147,13159,13171,13186,13198,13208,13218,13231,13241,13252,13262],{"slug":13134,"name":13134,"fn":13135,"description":13136,"org":13137,"tags":13138,"stars":13144,"repoUrl":13145,"updatedAt":13146},"aggregation","perform data aggregation in TanStack Table","Aggregate TanStack Table columns independently of grouping, including grand totals, caller-selected row totals, multiple keyed aggregations, custom context-based definitions, grouped merges, manual values, and worker constraints.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13139,13142,13143],{"name":13140,"slug":13141,"type":15},"Data Analysis","data-analysis",{"name":13098,"slug":13099,"type":15},{"name":9,"slug":8,"type":15},28175,"https:\u002F\u002Fgithub.com\u002FTanStack\u002Ftable","2026-07-30T05:25:59.429787",{"slug":13148,"name":13148,"fn":13149,"description":13150,"org":13151,"tags":13152,"stars":13144,"repoUrl":13145,"updatedAt":13158},"api-not-found","diagnose TanStack Table API errors","Diagnose missing TanStack Table v9 exports, options, state slices, and instance methods. Load before inventing an API when code sees a type error, undefined feature method, absent object key, adapter mismatch, or v8-shaped example.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13153,13156,13157],{"name":13154,"slug":13155,"type":15},"Debugging","debugging",{"name":13098,"slug":13099,"type":15},{"name":9,"slug":8,"type":15},"2026-07-30T05:26:05.418735",{"slug":13160,"name":13160,"fn":13161,"description":13162,"org":13163,"tags":13164,"stars":13144,"repoUrl":13145,"updatedAt":13170},"cell-selection","select rectangular cell ranges in tables","Select rectangular cell ranges with cellSelectionFeature: two-corner range state keyed by row and column id, mousedown\u002Fmouseenter handlers, selection edges, render-order resolution under pinning, and autoResetCellSelection. Load when ranges widen unexpectedly after sorting or column reordering, when a drag re-renders the whole table, or when building copy-to-clipboard from a selection.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13165,13166,13167],{"name":13140,"slug":13141,"type":15},{"name":9,"slug":8,"type":15},{"name":13168,"slug":13169,"type":15},"UI Components","ui-components","2026-07-30T05:25:38.403427",{"slug":13172,"name":13172,"fn":13173,"description":13174,"org":13175,"tags":13176,"stars":13144,"repoUrl":13145,"updatedAt":13185},"client-vs-server","manage TanStack Table data pipelines","Choose client or server ownership for filtering, grouping, sorting, expanding, and pagination in TanStack Table v9. Load for manual* flags, mixed pipelines, server counts, or deciding which dataset each row-model stage receives.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13177,13180,13181,13184],{"name":13178,"slug":13179,"type":15},"Data Pipeline","data-pipeline",{"name":13098,"slug":13099,"type":15},{"name":13182,"slug":13183,"type":15},"Performance","performance",{"name":9,"slug":8,"type":15},"2026-07-30T05:25:45.400104",{"slug":13187,"name":13187,"fn":13188,"description":13189,"org":13190,"tags":13191,"stars":13144,"repoUrl":13145,"updatedAt":13197},"column-faceting","build faceted filter UIs","Build faceted filter UIs with columnFacetingFeature, facetedRowModel, facetedUniqueValues, and facetedMinMaxValues. Load for facet counts, numeric ranges, own-filter exclusion, or server-page facet completeness.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13192,13195,13196],{"name":13193,"slug":13194,"type":15},"Data Visualization","data-visualization",{"name":13098,"slug":13099,"type":15},{"name":9,"slug":8,"type":15},"2026-07-30T05:25:41.397257",{"slug":13199,"name":13199,"fn":13200,"description":13201,"org":13202,"tags":13203,"stars":13144,"repoUrl":13145,"updatedAt":13207},"column-filtering","implement column filtering in TanStack Table","Filter columns with columnFilteringFeature, filteredRowModel, filterFns, filterMeta, nested-row direction, and manualFiltering. Load for accessor compatibility, controlled filter updaters, fuzzy metadata, or client\u002Fserver ownership.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13204,13205,13206],{"name":13140,"slug":13141,"type":15},{"name":13098,"slug":13099,"type":15},{"name":9,"slug":8,"type":15},"2026-07-30T05:25:53.391632",{"slug":13209,"name":13209,"fn":13210,"description":13211,"org":13212,"tags":13213,"stars":13144,"repoUrl":13145,"updatedAt":13217},"column-ordering","manage TanStack Table column ordering","Control TanStack Table v9 leaf columnOrder with stable IDs while accounting for pinning regions, visibility, and groupedColumnMode precedence. Load for drag-and-drop columns or rendered order that differs from state.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13214,13215,13216],{"name":13098,"slug":13099,"type":15},{"name":9,"slug":8,"type":15},{"name":13168,"slug":13169,"type":15},"2026-07-30T05:26:03.37801",{"slug":13219,"name":13219,"fn":13220,"description":13221,"org":13222,"tags":13223,"stars":13144,"repoUrl":13145,"updatedAt":13230},"column-pinning","configure column pinning in TanStack Table","Pin columns into logical start, center, and end regions with columnPinningFeature and renderer-owned sticky CSS. Load for RTL offsets, z-index, backgrounds, overflow, widths, gaps, or overlaps.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13224,13227,13228,13229],{"name":13225,"slug":13226,"type":15},"CSS","css",{"name":13098,"slug":13099,"type":15},{"name":9,"slug":8,"type":15},{"name":13168,"slug":13169,"type":15},"2026-07-30T05:25:55.377366",{"slug":13232,"name":13232,"fn":13233,"description":13234,"org":13235,"tags":13236,"stars":13144,"repoUrl":13145,"updatedAt":13240},"column-resizing","implement column resizing in TanStack Table","Wire columnResizingFeature, header.getResizeHandler, resize mode and direction, pointer or touch events, and performant CSS-variable updates. Load when resize state changes but widths do not, or large tables resize slowly.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13237,13238,13239],{"name":13098,"slug":13099,"type":15},{"name":9,"slug":8,"type":15},{"name":13168,"slug":13169,"type":15},"2026-07-30T05:25:51.400011",{"slug":13242,"name":13242,"fn":13243,"description":13244,"org":13245,"tags":13246,"stars":13144,"repoUrl":13145,"updatedAt":13251},"column-sizing","configure column sizing in TanStack Table","Use columnSizingFeature numeric size, minSize, maxSize, getSize, getStart, getAfter, and total-size APIs in table, grid, or flex CSS. Load for auto or percentage misconceptions and sizing\u002Fpinning layout mismatch.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13247,13248,13249,13250],{"name":13225,"slug":13226,"type":15},{"name":13098,"slug":13099,"type":15},{"name":9,"slug":8,"type":15},{"name":13168,"slug":13169,"type":15},"2026-07-30T05:25:48.703799",{"slug":13253,"name":13253,"fn":13254,"description":13255,"org":13256,"tags":13257,"stars":13144,"repoUrl":13145,"updatedAt":13261},"column-visibility","manage column visibility in TanStack Table","Hide columns with columnVisibilityFeature while rendering visibility-aware header, column, and cell collections. Load when hidden columns remain in the DOM, false-versus-absent state is confused, or enableHiding is misunderstood.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13258,13259,13260],{"name":13098,"slug":13099,"type":15},{"name":9,"slug":8,"type":15},{"name":13168,"slug":13169,"type":15},"2026-07-30T05:25:47.367943",{"slug":13263,"name":13263,"fn":13264,"description":13265,"org":13266,"tags":13267,"stars":13144,"repoUrl":13145,"updatedAt":13271},"core","build data grids with TanStack Table","Use TanStack Table v9 as a headless data-grid state and row-processing engine. Load for first-table architecture, stable data and columns, row numbering with getDisplayIndex, semantic rendering, framework adapter choice, or deciding what Table owns versus the renderer.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[13268,13269,13270],{"name":13140,"slug":13141,"type":15},{"name":13098,"slug":13099,"type":15},{"name":13168,"slug":13169,"type":15},"2026-07-30T05:25:52.366295",125]