agents_simulate_inbound

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

High RISK CLASS

Category Execute

Parameters 80 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-simulate-inbound.md

WHAT IT DOES

What agents_simulate_inbound does on Dialogbrain

AI agents invoke agents_simulate_inbound to trigger actions in Dialogbrain. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.

Parameter	Type	Required	Description
`send_mode`	string	—	How the matched agent should deliver its reply. 'draft' (default, safe) creates a draft only — no real send, no idempotency key. 'auto' lets the agent deliver t
`thread_id`	integer	—	Thread ID to route the simulated event from. Must belong to the API key's workspace. Omit and set create_new_livechat=true to test on a FRESH thread with no his
`message_text`	string	—	Inbound message body to simulate. Defaults to '[MCP simulation test]' when omitted.
`system_message`	object	—	Tag the simulated inbound as a system/service-message row (missed call, group join, pinned message, etc.) so the `excluded_system_message_kinds` trigger filter
`blockchain_tx_data`	object	—	When set, simulate a blockchain:transfer event instead of a channel:message:new event. Expected keys: chain, to_address / from_address, tx_hash.
`channel_account_id`	integer	—	Optional. The livechat widget's channel_account_id to host the fresh chat when create_new_livechat=true. Omit to auto-pick the workspace's active livechat widge
`attachment_file_ids`	array	—	Optional list of workspace file IDs to attach to the simulated inbound message — same shape as a real Telegram message with image/document attachments. Use this
`create_new_livechat`	boolean	—	Start a FRESH, history-free livechat chat instead of using an existing thread_id — creates a new visitor + thread on the workspace's livechat widget and routes

Parameters from the server's own tool schema.

RISK

Why agents_simulate_inbound is rated High

This tool triggers external operations (message routing through agents, potential delivery to messaging platforms like Telegram and email) whose effects depend on arguments (send_mode parameter, thread selection, message content). While it defaults to draft mode (reducing risk), the explicit capability to execute real message delivery via send_mode='auto' makes this an Execute tool.

From the tool's definition Tool description states it 'Replay[s] an inbound message on a thread through the real trigger pipeline' and 'let the matched agent actually deliver' messages to Telegram/email when send_mode='auto'.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs agents_simulate_inbound 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_simulate_inbound, this is the rule to start with:

agents_simulate_inbound Rate-limited

agents_simulate_inbound stays usable, but rate-capped: a runaway agent can't fire it dozens of times 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_simulate_inbound": {
      "limits": [
        {
          "counter": "agents_simulate_inbound_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-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_simulate_inbound call is checked against it from then on.

FAQ

Questions about agents_simulate_inbound

What does the agents_simulate_inbound tool do? +

Replay an inbound message on a thread through the real trigger pipeline and return what would have happened. The router auto-picks the winning enabled agent + trigger by priority/specificity (same logic as production). By default send_mode='draft' so no real message is sent; pass send_mode='auto' on a test account to let the matched agent actually deliver (drafts get overwritten by the next draft, so 'auto' is the only way to verify Telegram/email delivery end-to-end). Use to verify routing for a thread: which agent answers, which trigger wins, or — when nothing matches — the structured skip reason. Pass blockchain_tx_data instead of message_text to simulate a blockchain:transfer event on the thread. Returns: {matched: true, matched_agent: {id, name, execution_mode}, matched_trigger: {id, trigger_type, conditions, specificity_score}, routing_reason, response_text, messages[], execution_mode, send_mode, model_used, tokens_input, tokens_output, latency_ms, rag_queries_made, rag_results_used} on a hit, or {matched: false, skip_reason, simulator_warnings} on a miss. It is categorised as a Execute tool in the Dialogbrain MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

What parameters does agents_simulate_inbound accept? +

agents_simulate_inbound accepts 8 parameters: send_mode, thread_id, message_text, system_message, blockchain_tx_data, channel_account_id, attachment_file_ids, create_new_livechat. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on agents_simulate_inbound? +

Register the Dialogbrain MCP server in PolicyLayer and add a rule for agents_simulate_inbound: 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_simulate_inbound? +

agents_simulate_inbound is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit agents_simulate_inbound? +

Yes. Add a rate_limit block to the agents_simulate_inbound 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_simulate_inbound completely? +

Set action: deny in the PolicyLayer policy for agents_simulate_inbound. 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_simulate_inbound? +

agents_simulate_inbound 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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