update_saved_view
Change a saved set of search filters: its name, the sorting, the filters. filters is the WHOLE list it keeps — list_saved_views shows the ones it has; send those you keep together with the new ones.
This record as markdown: /tools/com-quintadb-mcp/update-saved-view.md
What update_saved_view does on QuintaDB
AI agents use update_saved_view to create or update resources in QuintaDB, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your QuintaDB environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
app_id | string | Yes | Project ID (from list_projects) |
filters | array | — | field, condition (equal, not_equal, contains, greater_than, less_than, is_empty …), value, join (and / or) |
sort_by | string | — | A field's name or id |
view_id | integer | Yes | From list_saved_views |
new_name | string | — | |
order_asc | boolean | — |
Parameters from the server's own tool schema.
Why update_saved_view is rated Medium
An AI agent can call update_saved_view faster than any human can review: one bad instruction and it creates or modifies resources in QuintaDB by the hundred, each call as confident as the last.
Attacks that exploit this kind of access
The rule that runs update_saved_view safely
PolicyLayer is an MCP gateway: it sits between your AI agents and QuintaDB, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For update_saved_view, this is the rule to start with:
update_saved_view 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 QuintaDB, apply this rule, and every update_saved_view call is checked against it from then on.
Questions about update_saved_view
Change a saved set of search filters: its name, the sorting, the filters. filters is the WHOLE list it keeps — list_saved_views shows the ones it has; send those you keep together with the new ones. It is categorised as a Write tool in the QuintaDB MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
update_saved_view accepts 6 parameters: app_id, filters, sort_by, view_id, new_name, order_asc. Required: app_id, view_id. The full parameter table on this page comes from the server's own tool schema.
Register the QuintaDB MCP server in PolicyLayer and add a rule for update_saved_view: 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 QuintaDB. Nothing to install.
update_saved_view 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 update_saved_view 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 update_saved_view. 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.
update_saved_view is provided by the QuintaDB MCP server (https://quintadb.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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