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MCP

MCP Client Input

An MCP server can stop a tool call. The server asks for input.

  • A form needs an answer from the user.
  • A sampling request needs a model reply.

You want that request in the UI, and you want the answer to reach the server. chat() ends the run with an interrupt. outcome.type is interrupt. The payload has kind and request. When the user answers, the next run calls the tool again with the answer.

Read the pause

Pass the MCP client to chat() in mcp.clients. MCP Server Tools has that setup.

The stream ends on one RUN_FINISHED chunk. Read the interrupt there:

ts
import { chat } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { createMCPClient } from '@tanstack/ai-mcp'

const messages = [{ role: 'user' as const, content: 'Ask the server' }]

const mcp = await createMCPClient({
  transport: {
    type: 'http',
    url: 'https://my-mcp-server.example.com/mcp',
  },
})

const stream = chat({
  adapter: openaiText('gpt-5.5'),
  messages,
  mcp: { clients: [mcp] },
})

for await (const chunk of stream) {
  if (chunk.type !== 'RUN_FINISHED') continue
  if (chunk.outcome?.type !== 'interrupt') continue

  for (const interrupt of chunk.outcome.interrupts) {
    if (interrupt.reason !== 'mcp_input') continue
    const payload = interrupt.metadata?.['tanstack:interruptPayload']
    console.log(interrupt.id, payload)
  }
}

The pause follows these rules:

  • The input call has no tool result.
  • The run does not emit RUN_ERROR.
  • When other tools finish in that turn, the stream has those results.

The interrupt has these fields:

  • id: mcp_input_ and the tool call id
  • reason: mcp_input
  • message: Input required to run and the tool name
  • metadata.toolName: the tool name
  • metadata['tanstack:interruptPayload']: kind and request

kind is form or sampling. request is the MCP input body.

Answer the request

The answer comes back in a new request. Pass parentRunId and resume from that request to chat():

ts
// app/api/chat/route.ts
import {
  chat,
  chatParamsFromRequest,
  toServerSentEventsResponse,
} from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { createMCPClient } from '@tanstack/ai-mcp'

export async function POST(request: Request) {
  const params = await chatParamsFromRequest(request)
  const mcp = await createMCPClient({
    transport: {
      type: 'http',
      url: 'https://my-mcp-server.example.com/mcp',
    },
  })

  const stream = chat({
    adapter: openaiText('gpt-5.5'),
    messages: params.messages,
    threadId: params.threadId,
    runId: params.runId,
    ...(params.parentRunId ? { parentRunId: params.parentRunId } : {}),
    ...(params.resume ? { resume: params.resume } : {}),
    mcp: { clients: [mcp] },
  })
  return toServerSentEventsResponse(stream)
}

Then answer the request in the UI:

  1. Read interrupts from useChat.
  2. Find the item where reason is mcp_input.
  3. Read metadata['tanstack:interruptPayload'] and show the request.
  4. Call resolveInterrupt with the answer, or call cancel().
tsx
import { useState } from 'react'
import { fetchServerSentEvents, useChat } from '@tanstack/ai-react'

function isRecord(value: unknown): value is Record<string, unknown> {
  return typeof value === 'object' && value !== null && !Array.isArray(value)
}

function readMcpInput(
  metadata: Readonly<Record<string, unknown>> | undefined,
) {
  const payload = metadata?.['tanstack:interruptPayload']
  if (!isRecord(payload)) return undefined
  const kind = payload.kind
  if (kind !== 'form' && kind !== 'sampling') return undefined
  if (!isRecord(payload.request)) return undefined
  return { kind, request: payload.request }
}

export function McpInputPrompt() {
  const [answer, setAnswer] = useState('')
  const { interrupts } = useChat({
    threadId: 'thread-1',
    connection: fetchServerSentEvents('/api/chat'),
  })

  return (
    <>
      {interrupts.map((interrupt) => {
        if (interrupt.reason !== 'mcp_input') return null
        if (interrupt.kind !== 'generic') return null
        const input = readMcpInput(interrupt.metadata)
        if (!input) return null
        return (
          <form
            key={interrupt.id}
            onSubmit={(event) => {
              event.preventDefault()
              interrupt.resolveInterrupt({ value: answer })
            }}
          >
            <label>
              {String(input.request.message ?? interrupt.message)}
              <input
                value={answer}
                onChange={(event) => setAnswer(event.target.value)}
              />
            </label>
            <button type="submit">Send</button>
            <button type="button" onClick={() => interrupt.cancel()}>
              Cancel
            </button>
          </form>
        )
      })}
    </>
  )
}

useChat sends the answer in a new run. Then chat() runs the tool again, and the MCP client sends the answer to the server.

What to send

The answer depends on kind.

  • form: send an object that matches request.requestedSchema. A server from createMCPServer asks for { value: string }. The MCP client sends the object as the accepted content.
  • sampling: send the model reply as a string. You can also send a full MCP CreateMessageResult.
  • cancel(): for a form, the server gets { action: 'cancel' }. A createMCPServer tool then ends with a tool error. For a sampling request, the tool call ends with an error.

To decline a form, send the full MCP answer: { action: 'decline' }.

A resumed tool gets the answer on ctx.inputResponse. The MCP tools read it for you. Your own server tool can read it too.

The client answers one input request per tool call. If the server asks for input again after it gets the answer, the tool call ends with a tool error.

The pause and the resume work on spec 2026. On spec 2025, the server asks the client for input in the middle of the tool call. chat() cannot pause that call, so the tool call fails.

A tool error

If the thrown value is a plain Error, the result is a tool error. The run does not pause.

chat() pauses only for this shape:

  • name: MCPInputRequiredError
  • kind: form or sampling
  • request: the MCP input body

Outside chat()

Outside chat(), the client throws MCPInputRequiredError. The name ask is the tool name on your server.

  1. Call tools() on the MCP client.
  2. Call execute on the tool.
  3. Check the error with isMCPInputRequiredError, then read kind and request.
  4. Close the client after the call.
ts
import { createMCPClient, isMCPInputRequiredError } from '@tanstack/ai-mcp'

export async function callAsk() {
  const mcp = await createMCPClient({
    transport: {
      type: 'http',
      url: 'https://my-mcp-server.example.com/mcp',
    },
  })

  try {
    const tools = await mcp.tools()
    const ask = tools.find((tool) => tool.name === 'ask')
    if (!ask?.execute) return
    await ask.execute({})
  } catch (error) {
    if (!isMCPInputRequiredError(error)) throw error
    console.log(error.kind, error.request)
  } finally {
    await mcp.close()
  }
}
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