artifacts_update
Republish an existing artifact with new HTML. The slug and URL stay the SAME; a new version is stored (older versions are retained up to a cap). Use this to refresh a shared dashboard — anyone with the link sees the new snapshot.
This record as markdown: /tools/io-github-saloprj-dialogbrain/artifacts-update.md
What artifacts_update does on Dialogbrain
AI agents use artifacts_update 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 |
|---|---|---|---|
html | string | Yes | The new full self-contained HTML page (same contract as artifacts.create). |
label | string | — | Optional human name for this version (e.g. 'Q3 final'). |
template | string | — | Optional: attach/replace the data-driven template (HTML with {{placeholder}} tokens) on this existing artifact, so later artifacts.refresh(data={...}) can re-re |
artifact_id | integer | Yes | The artifact to republish. |
Parameters from the server's own tool schema.
Why artifacts_update is rated Medium
This tool modifies existing data (an artifact's HTML content) reversibly by storing new versions while retaining older ones. It is not destructive (versions are kept, not deleted), not financial, and not executable code. It is a write operation that updates content. Severity is medium because modifying shared dashboard content could affect multiple viewers, but the action is versioned and reversible.
From the tool's definition Republish an existing artifact with new HTML. The slug and URL stay the SAME; a new version is stored (older versions are retained up to a cap). Use this to refresh a shared dashboard — anyone with the link sees the new snapshot.
Risk signalsAccepts raw HTML/template content (html)
Attacks that exploit this kind of access
The rule that runs artifacts_update 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_update, this is the rule to start with:
artifacts_update 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_update call is checked against it from then on.
Questions about artifacts_update
Republish an existing artifact with new HTML. The slug and URL stay the SAME; a new version is stored (older versions are retained up to a cap). Use this to refresh a shared dashboard — anyone with the link sees the new snapshot. 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_update accepts 4 parameters: html, label, template, artifact_id. Required: html, 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_update: 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_update 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_update 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_update. 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_update 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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