artifacts_refresh
Re-render a data-driven artifact from a small data payload and publish a new version at the SAME URL. The artifact must have been created/updated with a template (HTML containing {{placeholder}} tokens). Pass data as a flat map of placeholder -> value (e.g. {"leads": "3 200", "date": "15 июля 202...
This record as markdown: /tools/io-github-saloprj-dialogbrain/artifacts-refresh.md
What artifacts_refresh does on Dialogbrain
AI agents use artifacts_refresh 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 |
|---|---|---|---|
data | object | Yes | Flat map of {{placeholder}} name -> value. Every placeholder in the template must be present. Values are HTML-escaped. |
label | string | — | Optional version label (e.g. 'daily refresh'). |
artifact_id | integer | Yes | The data-driven artifact to refresh. |
Parameters from the server's own tool schema.
Why artifacts_refresh is rated Medium
An AI agent can call artifacts_refresh faster than any human can review: one bad instruction and it creates or modifies resources in Dialogbrain by the hundred, each call as confident as the last.
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs artifacts_refresh 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 artifacts_refresh, this is the rule to start with:
artifacts_refresh 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Dialogbrain, apply this rule, and every artifacts_refresh call is checked against it from then on.
Questions about artifacts_refresh
Re-render a data-driven artifact from a small data payload and publish a new version at the SAME URL. The artifact must have been created/updated with a template (HTML containing {{placeholder}} tokens). Pass data as a flat map of placeholder -> value (e.g. {"leads": "3 200", "date": "15 июля 2026"}); the server substitutes them into the stored template — you do NOT send any HTML. Ideal for scheduled refreshes of live numbers. Every template placeholder must have a value in data, or the call fails. 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.
artifacts_refresh accepts 3 parameters: data, label, artifact_id. Required: data, artifact_id. The full parameter table on this page comes from the server's own tool schema.
Register the Dialogbrain MCP server in PolicyLayer and add a rule for artifacts_refresh: 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.
artifacts_refresh is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the artifacts_refresh 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.
Set action: deny in the PolicyLayer policy for artifacts_refresh. 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.
artifacts_refresh 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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