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Delegate a task and wait

ask_agent

Delegate a self-contained task or question to another installed AI agent and receive its answer in one synchronous call, passing a project path when the work involves local code.

Instructions

Hand a task to another AI agent and wait for its answer. One call does the whole thing. The agent starts fresh with no knowledge of this conversation and cannot see your files unless you point it at them, so make the prompt self-contained and pass workingDirectory when the task is about code in a project. If "succeeded" is false, read "diagnostics". For a job long enough to outlast this request, use start_task.

Input Schema

NameRequiredDescriptionDefault
agentYesWhich agent to use, exactly as list_agents names it, e.g. "gemini".
promptYesThe full self-contained task or question for that agent.
workingDirectoryNoAbsolute path to run the agent in, so it can read the project files.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does most of it: it discloses that the remote agent starts with no conversational context and no filesystem visibility unless pointed at a path, and that failure surfaces as "succeeded": false with a "diagnostics" field. It leaves out timeout behavior, cost/latency implications, and concurrency, which keeps it from a 5.

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?

Five short sentences, front-loaded with what the tool does and the blocking behavior, then constraints, then the failure path, then the sibling escape hatch. Nothing is redundant with the title or schema.

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?

There is no output schema, yet the description tells the agent how to interpret the result (check "succeeded", read "diagnostics") and how the delegated agent's isolated context must be compensated for. For a 3-parameter tool with full schema coverage, nothing operationally necessary is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the schema does the baseline work and 3 would be acceptable. The description still adds real semantics the schema lacks: why the prompt must be self-contained (the remote agent is context-free) and when to supply workingDirectory (code work in a project).

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?

States a specific verb and resource — hand a task to another AI agent — plus the key behavioral fact that the call blocks and waits for the answer. This contrasts cleanly with the sibling start_task, which is fire-and-forget, so an agent can choose between them without opening either schema.

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

Usage Guidelines5/5

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

Gives an explicit routing rule: use this for the synchronous case, use start_task "for a job long enough to outlast this request." It also gives a conditional instruction for workingDirectory (pass it when the task concerns code in a project), so both the choice of tool and the choice of parameters are guided.

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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