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ai-openrouter: malformed Chat Completions tool arguments execute as an empty object

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@tombeckenham is already working on this.

Since Oct 9, 2026.

  • #1690 by @PatrM — open

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This issue has not been assessed yet.

Description

has-pr waiting-on: maintainer
TanStack AI version
  • @tanstack/ai: 0.66.0
  • @tanstack/ai-openrouter: 0.21.2

Also seen on 0.64.1 / 0.20.3. The adapter code is unchanged on main at 377262c0b4e5f59f8fd467a831b9341cc705077e.

Framework/Library version

No UI framework. Node.js 24.18.0, Zod 4.6.5.

Describe the bug and the steps to reproduce it

openRouterText can execute a tool after its streamed arguments fail JSON parsing. On a parse failure the adapter sets TOOL_CALL_END.input to {}, which replaces the original arguments before the core parses them.

#1602 fixed the same fallback in @tanstack/openai-base (#1601). ai-openrouter keeps its own copy of the Chat Completions stream handling, so the fix did not carry over.

OpenRouter produces this input in normal operation. When a model hits its output limit inside a tool call, OpenRouter ends the stream with finish_reason: "tool_calls" (native_finish_reason: "max_output_tokens"), then sends the usage chunk and [DONE]. The arguments are cut off, for example {"sql":"SELECT g.brand, g.model, SUM(s.quantity) AS units.

Steps to reproduce

  1. Define a tool with an optional field: inputSchema: z.object({ sql: z.string().optional() }).
  2. Call chat() with openRouterText('openai/gpt-4.1-mini'), the tool, toolChoice forcing it, and maxCompletionTokens: 12.

A controlled SSE response that sends {"sql": "SELECT 1" with finish_reason: "tool_calls" reproduces it without a provider.

Actual behavior

The handler runs once with {}. The next model request contains a successful tool result. With a required field, the model gets Invalid input: expected string, received undefined instead of a JSON parse error, and the raw arguments are lost.

Expected behavior

Return a tool-result error for malformed JSON without calling the tool, as docs/tools/server-tools.md describes and as openai-base does since #1602.

Cause

Both the finish_reason path and the end-of-body drain set parsedInput = {} after a parse failure. completeToolCall then serializes that object over the original argument string, so the core's malformed-argument check never sees the invalid JSON.

Your Minimal, Reproducible Example - (Sandbox Highly Recommended)

Self-contained script, no API key. A local SSE server replays the OpenRouter stream described above.

npm init -y && npm pkg set type=module && npm i @tanstack/[email protected] @tanstack/[email protected] [email protected]
node index.mjs

Output:

TOOL_CALL_END input: {}
tool executed with: [{}]
tool result sent to the model: tool ran
index.mjs 
// openRouterText runs a tool with {} when its streamed arguments are not valid JSON.
// OpenRouter sends this when a model hits its output limit inside a tool call:
// truncated arguments with finish_reason "tool_calls" (native_finish_reason "max_output_tokens").
// A local SSE server replays that stream, so no API key is needed.
import http from 'node:http'
import { chat, toolDefinition } from '@tanstack/ai'
import { createOpenRouterText } from '@tanstack/ai-openrouter'
import { z } from 'zod'

const chunk = (choice, extra = {}) =>
  `data: ${JSON.stringify({
    id: 'gen-1',
    object: 'chat.completion.chunk',
    created: 1,
    model: 'openai/gpt-4.1-mini',
    choices: [{ index: 0, delta: {}, finish_reason: null, ...choice }],
    ...extra,
  })}\n\n`

const truncatedToolCall = [
  chunk({ delta: { role: 'assistant', content: '' } }),
  chunk({
    delta: {
      tool_calls: [
        {
          index: 0,
          id: 'call_1',
          type: 'function',
          function: { name: 'run_query', arguments: '{"sql":"SELECT brand, model, SUM(units' },
        },
      ],
    },
  }),
  chunk({ finish_reason: 'tool_calls', native_finish_reason: 'max_output_tokens' }),
  chunk(
    { finish_reason: 'tool_calls', native_finish_reason: 'max_output_tokens' },
    { usage: { prompt_tokens: 10, completion_tokens: 12, total_tokens: 22 } },
  ),
  'data: [DONE]\n\n',
]
const followUp = [
  chunk({ delta: { role: 'assistant', content: 'Done.' } }),
  chunk({ finish_reason: 'stop' }, { usage: { prompt_tokens: 20, completion_tokens: 2, total_tokens: 22 } }),
  'data: [DONE]\n\n',
]

const providerRequests = []
const server = http.createServer((req, res) => {
  let body = ''
  req.on('data', (d) => (body += d))
  req.on('end', () => {
    providerRequests.push(JSON.parse(body))
    res.writeHead(200, { 'content-type': 'text/event-stream' })
    for (const c of providerRequests.length === 1 ? truncatedToolCall : followUp) res.write(c)
    res.end()
  })
})
await new Promise((resolve) => server.listen(0, resolve))

const executedInputs = []
const runQuery = toolDefinition({
  name: 'run_query',
  description: 'Run a SQL query',
  inputSchema: z.object({ sql: z.string().optional() }),
}).server((input) => {
  executedInputs.push(input)
  return 'tool ran'
})

const adapter = createOpenRouterText('openai/gpt-4.1-mini', 'sk-dummy', {
  serverURL: `http://127.0.0.1:${server.address().port}/api/v1`,
})

for await (const event of chat({
  adapter,
  tools: [runQuery],
  messages: [{ role: 'user', content: 'Top 20 guitars by revenue in 2025?' }],
})) {
  if (event.type === 'TOOL_CALL_END') console.log('TOOL_CALL_END input:', JSON.stringify(event.input))
}
server.close()

const toolMessage = providerRequests[1]?.messages.find((m) => m.role === 'tool')
console.log('tool executed with:', JSON.stringify(executedInputs))
console.log('tool result sent to the model:', toolMessage?.content)
console.log(
  executedInputs.length > 0
    ? '\nBUG: the tool ran with {} although its arguments were malformed JSON.'
    : '\nOK: the tool did not run; the model got a tool error.',
)
Screenshots or Videos (Optional)

n/a

Do you intend to try to help solve this bug with your own PR?

Yes, I think I know how to fix it and will discuss it in the comments of this issue

Terms & Code of Conduct
  • I agree to follow this project's Code of Conduct
  • I understand that if my bug cannot be reliable reproduced in a debuggable environment, it will probably not be fixed and this issue may even be closed.
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