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sushiHex
by sushiHex

ask_claude_async

Dispatch Claude tasks to run in the background, returning immediately. Retrieve outputs later via the agent's inbox for concurrent or fire-and-forget AI operations.

Instructions

Dispatch a Claude task in the background; returns immediately.

Runs the same ask_claude in a background thread, then delivers the result through the existing mailbox as a message from "claude" to from_agent — poll it with inbox(agent=from_agent). The delivered message body is the JSON-encoded ask_claude result, plus label if supplied (use it to match results when firing several concurrent dispatches). from_agent must be a known agent. Fire-and-forget: not persisted, so a hardline-mcp restart before completion loses the task.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelNo
modelNo
writeNo
effortNodefault
promptYes
workdirNo
from_agentYes
Behavior5/5

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

With no annotations, the description carries full burden and delivers richly: background execution, immediate return, mailbox delivery from 'claude', JSON-encoded body, label support, fire-and-forget semantics, non-persistence, and restart loss are all disclosed. It even notes from_agent must be a known agent.

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

Conciseness4/5

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

The description is front-loaded with the core purpose and grouped logically into delivery, polling, and caveats. It is longer than strictly necessary, but each sentence contributes meaningful operational detail, so no content is wasted.

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

Completeness3/5

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

The response flow is well covered: what comes back, how to poll, and how to match concurrent results. However, with no output schema and no annotations, the missing parameter-level semantics for most arguments leaves the description incomplete for a 7-parameter tool.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must explain parameters, but it only clarifies label (matching results) and from_agent (must be known). The five other parameters (model, write, effort, prompt, workdir) are left largely unexplained, though prompt's meaning is implied by 'Claude task'.

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 opens with 'Dispatch a Claude task in the background; returns immediately,' which names a specific verb, resource, and key behavioral distinction from the synchronous ask_claude sibling. This clearly differentiates the tool from ask_claude, ask_hermes, and ask_codex.

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 explains when to use this async variant: 'Runs the same ask_claude in a background thread' and instructs polling via inbox(agent=from_agent). It does not explicitly name alternatives for exclusion, but the contrast with ask_claude is evident, and the delivery mechanism is well specified.

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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