prompts_prompt_restore
Restore a past version of a prompt template by version_number. Creates a new version pointing at the restored content — history is preserved. Fans out to every agent using this template without a per-agent override; the response includes affected_agents as a receipt of the fan-out.
This record as markdown: /tools/io-github-saloprj-dialogbrain/prompts-prompt-restore.md
What prompts_prompt_restore does on Dialogbrain
AI agents use prompts_prompt_restore 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 |
|---|---|---|---|
reason | string | — | Optional: why this restore is happening (shows up in history UI) |
prompt_id | integer | Yes | ID of the prompt template |
version_number | integer | Yes | The version_number to restore (get it from prompts.prompt_history) |
Parameters from the server's own tool schema.
Why prompts_prompt_restore is rated Medium
This tool creates a new version of a prompt template and propagates that change to every agent using it. While it preserves history (making it reversible in principle), it is fundamentally a Write operation that modifies data and triggers distributed side effects across multiple AI agents.
From the tool's definition Tool description states 'Restore a past version of a prompt template' and 'Creates a new version pointing at the restored content' — this modifies prompt templates and broadcasts changes to multiple agents ('Fans out to every agent using this template').
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs prompts_prompt_restore 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 prompts_prompt_restore, this is the rule to start with:
prompts_prompt_restore 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 prompts_prompt_restore call is checked against it from then on.
Questions about prompts_prompt_restore
Restore a past version of a prompt template by version_number. Creates a new version pointing at the restored content — history is preserved. Fans out to every agent using this template without a per-agent override; the response includes affected_agents as a receipt of the fan-out. 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.
prompts_prompt_restore accepts 3 parameters: reason, prompt_id, version_number. Required: prompt_id, version_number. 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 prompts_prompt_restore: 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.
prompts_prompt_restore 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 prompts_prompt_restore 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 prompts_prompt_restore. 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.
prompts_prompt_restore 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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