updateUserPreferences
Update the calling user's preferences. Self-only. Rate limit 60/min. Partial: only fields supplied are updated. notifications upserts a single row; grids upserts per-row keyed by (user_id, project_id, grid_key, view_name).
This record as markdown: /tools/io-stablebaseline-sb/updateuserpreferences.md
What updateUserPreferences does on Stable Baseline
AI agents use updateUserPreferences to create or update resources in Stable Baseline, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Stable Baseline environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
grids | array | — | |
notifications | object | — |
Parameters from the server's own tool schema.
Why updateUserPreferences is rated Medium
This tool creates or modifies user preference data (notifications, grid configurations) in a reversible manner. It is scoped to the calling user only ('Self-only'), limiting blast radius. The rate limit (60/min) and partial update mechanism further constrain risk. No deletion, financial transaction, code execution, or external side effects occur.
From the tool's definition Tool name 'updateUserPreferences' and description 'Update the calling user's preferences' and 'only fields supplied are updated' indicates data modification.
Risk signalsHigh parameter count (18 properties)
Attacks that exploit this kind of access
The rule that runs updateUserPreferences safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Stable Baseline, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For updateUserPreferences, this is the rule to start with:
updateUserPreferences 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 Stable Baseline, apply this rule, and every updateUserPreferences call is checked against it from then on.
Questions about updateUserPreferences
Update the calling user's preferences. Self-only. Rate limit 60/min. Partial: only fields supplied are updated. notifications upserts a single row; grids upserts per-row keyed by (user_id, project_id, grid_key, view_name). It is categorised as a Write tool in the Stable Baseline MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
updateUserPreferences accepts 2 parameters: grids, notifications. The full parameter table on this page comes from the server's own tool schema.
Register the Stable Baseline MCP server in PolicyLayer and add a rule for updateUserPreferences: 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 Stable Baseline. Nothing to install.
updateUserPreferences 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 updateUserPreferences 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 updateUserPreferences. 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.
updateUserPreferences is provided by the Stable Baseline MCP server (https://api.stablebaseline.io/functions/v1/cloud-serve/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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