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call_agent_async

Launch agent tasks asynchronously in the background, get an immediate task ID, and fetch results later when needed without blocking your workflow.

Instructions

Start agent task in background (NON-BLOCKING). Returns task_id immediately.

Workflow:

  1. call_agent_async → get task_id instantly

  2. Continue your work (read files, think, etc.)

  3. get_agent_result(task_id, block=true) when ready

Why async is better:

  • No wasted waiting time

  • Can launch multiple searches in parallel

  • Check results when YOU need them

Parallel search pattern:

call_agent_async(explore, "Find X") → task_1
call_agent_async(researcher, "Find Y") → task_2
... do other work ...
get_agent_result(task_1, block=false)
get_agent_result(task_2, block=false)
... do other work ...
get_agent_result(task_1, block=true)
get_agent_result(task_2, block=true)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdYesWorking directory for the agent
agentYesThe agent to use: advisor | researcher | explore | frontend-ui-ux-engineer | document-writer
promptYesThe prompt/task for the agent
contextNoAdditional context
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 clearly discloses the NON-BLOCKING behavior, immediate return of task_id, and the necessity of later calling get_agent_result. It also demonstrates parallel usage. It does not cover failure modes or error handling, but the core behavioral traits are well addressed.

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 well-structured with clear sections (Workflow, Why async is better, Parallel search pattern) and front-loads the key behavior. It is slightly longer than necessary, but each section contributes to understanding the tool's usage pattern.

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

Completeness5/5

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

For an async launch tool without an output schema, the description is remarkably complete. It covers the return value (task_id), the full workflow including how to retrieve results with get_agent_result, and demonstrates a parallel search pattern. It integrates well with sibling tools and addresses the uncertainty of async behavior.

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 already provides 100% coverage with descriptions for all four parameters (agent, prompt, cwd, context). The description adds a usage example mapping agent and prompt to positional arguments, but does not enrich the meaning of cwd or context beyond 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 the tool starts an agent task in the background (NON-BLOCKING) and returns a task_id immediately. This specific verb+resource+behavior distinguishes it from siblings like call_agent (likely synchronous) and aligns with get_agent_result for retrieval.

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?

The description provides a clear workflow (start task, continue work, retrieve result with get_agent_result) and explains why async is better for parallel searches. It recommends this tool for explore/researcher use cases, but does not explicitly state when NOT to use it (e.g., when immediate results are needed versus call_agent).

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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