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chatgpt_webui_ask_async_start

Start a background ChatGPT task and receive a job ID immediately. Solve long-running Deep Research and Pro run delays by executing them asynchronously.

Instructions

Start a background ChatGPT ask job and return immediately with a job id. Use this for long-running tasks like Deep Research and Pro runs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoModel slug override. Examples: gpt-5-2, gpt-5-2-pro, gpt-5-1-instant, research. Ignored when deep_research=true.
promptYesPrompt to send.
workspaceNoPreferred workspace label if ChatGPT shows workspace selection (e.g. PRO, Personal).
model_modeNoQuick model mode selector that maps to GPT-5.2 variants.
create_imageNoEnable image-generation mode in ChatGPT UI.
deep_researchNoEnable Deep Research flow. When true, model selection switches to research mode.
conversation_idNoOptional conversation id to continue an existing chat.
wait_timeout_msNoMax wait for response completion in milliseconds (supports long GPT-5.2 Pro runs).
reasoning_effortNoUI reasoning control. Mainly relevant for thinking-capable models.
parent_message_idNoOptional parent message id for continued conversation state.
deep_research_site_modeNoOptional Deep Research sites mode override.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.6

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the burden of behavioral disclosure. It effectively discloses the key behavior: the tool starts a background job, does not wait for completion, and returns immediately with a job id. It could also mention that results are retrieved via the async_status/result siblings, but this is strongly implied by the sibling tool names.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler. The core behavior is front-loaded, and the usage guidance follows immediately. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 11 parameters and no output schema, the description tells the agent the essential invocation contract: it starts a background job and returns a job id. The full async workflow is not spelled out, but sibling tools and the return-immediately statement make it sufficiently complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% parameter description coverage, so the baseline is 3. The description adds no direct parameter meaning beyond referencing long-running and Deep Research/Pro use cases, which loosely maps to deep_research and model_mode but does not materially improve on the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action: start a background ChatGPT ask job and immediately return a job id. It also distinguishes this from synchronous ask and from the async status/result siblings by emphasizing background execution and long-running tasks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says to use this for long-running tasks like Deep Research and Pro runs, which gives clear guidance on when this tool is appropriate. It does not explicitly name a synchronous alternative or say when not to use it, but the context and sibling names make that inference straightforward.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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