agents_ask

Active

declared in 1.17.0

Send a message to an AI agent and get its response. The agent runs with its configured prompt, tools, and knowledge. Use this to test agents or have them process a task. Returns: {status: 'replied'|'silent', response_text, messages[], full_reply, model_used, tokens_*, send_mode, execution_mode, tool_calls[]}. `tool_calls[]` is the per-tool trace in call order — each {tool, success, error, duration_ms} — so you can see which tool the agent ran and why it failed (e.g. a workbench script error) directly from this response, no trace lookup needed. `messages[]` carries each messages.send invocation the agent made (text, subject, reply_to_message_id, timestamp, message_id, attachments=[{file_id,name,mime}]). `full_reply` concatenates text only — attachment-only sends show up in `messages` but not `full_reply`. `status='silent'` iff both response_text is empty AND messages is empty. Execution may take 10-60s depending on agent complexity.

Parameters schema

{
  "type": "object",
  "required": [
    "agent_id",
    "message"
  ],
  "properties": {
    "message": {
      "type": "string",
      "description": "Message/goal to send to the agent"
    },
    "agent_id": {
      "type": "integer",
      "description": "ID of the AI agent to ask"
    },
    "send_mode": {
      "type": "string",
      "description": "Send mode for the agent run: 'draft' = create drafts, 'auto' = send directly. Defaults to the agent's configured default_send_mode. Does NOT change execution_mode — that is fixed by the agent's config."
    }
  }
}

What this tool wraps· 0 endpoints

min confidence0.70 0.50

No endpoints wrapped at confidence ≥ 0.70.

Parent server

io.github.saloprj/dialogbrain

https://github.com/saloprj/dialogbrain-mcp

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