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ask_agent

Direct a prompt to a chosen AI agent such as Claude, Codex, Gemini, or Copilot, and receive the agent's response. Specify the target agent and question to get an answer.

Instructions

Ask a specific AI agent a question. Use when targeting a single agent like 'ask codex' or 'ask gemini'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentYesTarget agent to query
modelNoSpecific model to use (defaults to agent default)
promptYesThe question or prompt to send
contextNoAdditional context to pipe via stdin
timeoutNoTimeout in milliseconds (default: 120000)
analysisLevelNoAnalysis depth level (codex only)
Behavior2/5

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It only states the basic action and target, omitting any details about response format, side effects, permissions, rate limits, or error behavior. The description adds minimal context beyond what is self-evident from the name.

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?

The description is a single sentence with an immediate action verb and a usage example. Every word earns its place, and the key information is front-loaded. It is concise without sacrificing clarity.

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 tool has 6 parameters and no output schema, so the description should ideally explain what the tool returns or any caveats. It is adequate for a simple ask tool but lacks guidance on response handling and edge cases. The schema covers parameters, but the description fails to address missing output expectations.

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 schema has 100% description coverage for all 6 parameters, so the baseline is 3. The description itself does not add any parameter-specific meaning, but it also doesn't need to because the schema already documents each parameter clearly (e.g., 'agent' enum, 'timeout' default, 'analysisLevel' for codex only).

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's action ('Ask a specific AI agent a question') and explicitly distinguishes it from siblings by contrasting with 'ask codex'/'ask gemini' and implying a single target versus 'ask_all'. This meets the criteria of a specific verb+resource with sibling differentiation.

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 clear context for when to use the tool ('when targeting a single agent') and gives concrete examples. It does not explicitly exclude alternatives like 'ask_all' or 'delegate_task', but the guidance is sufficient for most scenarios, so it earns a 4.

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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