agents_create

SERVERDialogbrain SOURCEhttps://api.dialogbrain.com/mcp

Medium RISK CLASS

Category Write

Parameters 101 required

Recommended Rate-limitedsee the rule below

Registry record Grade D, identity unverified Pull the record →

This record as markdown: /tools/io-github-saloprj-dialogbrain/agents-create.md

WHAT IT DOES

What agents_create does on Dialogbrain

AI agents use agents_create to create or update resources in Dialogbrain, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Dialogbrain environment.

Parameter	Type	Required	Description
`name`	string	Yes	Name of the AI agent (1-100 characters)
`model`	string	—	LLM the agent runs on. OMIT to use the platform default (deepseek-chat). Applied right after creation so you don't need a follow-up agents_update.
`template`	string	—	Optional template slug to instantiate the agent + its built-in trigger from (deterministic). Use 'dm-auto-reply' for the customer DM auto-reply agent (incoming
`prompt_id`	integer	—	ID of the prompt to assign to this agent
`send_mode`	string	—	Default send mode: 'auto' or 'draft'. OMIT to use 'draft' (the default).
`description`	string	—	Optional description of what this agent does
`prompt_text`	string	—	Only with `template`: the agent's persona/instructions, stored inline on the agent (drives how it replies). No separate prompts tool needed.
`text_engine`	string	—	Text-execution engine: 'rule_based', 'ai_assisted', 'agentic' (default), or 'claude_channels'. Voice is derived from triggers, not engine. OMIT to use the defau
`allowed_tools`	array	—	Explicit allow-list of tool IDs the agent may call on triggered runs (e.g. ['workbench.run_python', 'messages.send']). OMIT to use system defaults. Applied righ
`max_iterations`	integer	—	Hard cap on agentic-loop turns per run (1-50). OMIT for the default (10).

Parameters from the server's own tool schema.

RISK

Why agents_create is rated Medium

This tool creates a new AI agent that can operate autonomously, send messages, execute multi-step automation, and interact with unified inbox channels (WhatsApp, Telegram, Email, voice).

From the tool's definition Create a new AI agent in the workspace

Risk signalsAccepts raw HTML/template content (template) · High parameter count (10 properties)

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs agents_create safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Dialogbrain, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For agents_create, this is the rule to start with:

agents_create Rate-limited

agents_create stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "agents_create": {
      "limits": [
        {
          "counter": "agents_create_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Dialogbrain, apply this rule, and every agents_create call is checked against it from then on.

FAQ

Questions about agents_create

What does the agents_create tool do? +

Create a new AI agent in the workspace. Execution modes: - ai_assisted (default, recommended): Two-phase AI — fast pre-classifier (Haiku) for keyword filtering and simple replies, then full AI with tools for complex messages. Best for: auto-replies, group monitoring, keyword-based filtering. - agentic: Autonomous multi-step agent with planning and tool execution. Best for: complex scheduled tasks, multi-step automation. - rule_based: Simple pattern matching without AI. For keyword filtering: use ai_assisted mode + set keywords in trigger conditions (free, deterministic) and/or auto_reply_rules (smart, LLM-based) via agents.update. From a template: pass template (e.g. 'dm-auto-reply') to create the agent AND its built-in trigger in one call — deterministic, no need to add a trigger separately. Pass prompt_text for the agent's persona (stored inline). When template is set, name/text_engine/send_mode come from the template. It is categorised as a Write tool in the Dialogbrain MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

What parameters does agents_create accept? +

agents_create accepts 10 parameters: name, model, template, prompt_id, send_mode, description, prompt_text, text_engine, allowed_tools, max_iterations. Required: name. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on agents_create? +

Register the Dialogbrain MCP server in PolicyLayer and add a rule for agents_create: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Dialogbrain. Nothing to install.

What risk level is agents_create? +

agents_create is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit agents_create? +

Yes. Add a rate_limit block to the agents_create rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block agents_create completely? +

Set action: deny in the PolicyLayer policy for agents_create. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides agents_create? +

agents_create is provided by the Dialogbrain MCP server (https://api.dialogbrain.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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