update_item
Edit fields on one or more saved items. Pass a single id or an array of ids for batch updates (same fields applied to all). For batch: attributes are REPLACED not merged, og:image fallback is skipped.
This record as markdown: /tools/com-grabbitapp-grabblist/update-item.md
What update_item does on Grabblist
AI agents use update_item to create or update resources in Grabblist, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Grabblist environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
id | object | Yes | Item ID or array of item IDs to update |
tags | array | — | Replace all tags with this list |
notes | string | — | User or AI notes — personal comments, thoughts, comparisons |
title | string | — | Item title |
images | array | — | All image URLs — replaces the entire images array |
reason | string | — | Why this change was made (shown to user in change history) |
status | string | — | Decision status: considering, shortlisted, decided, bought, rejected |
category | string | — | Item category |
quantity | integer | — | How many of this item |
image_url | object | — | Main image URL |
item_type | string | — | What kind of item this is |
attributes | object | — | Merge into existing attributes |
Parameters from the server's own tool schema.
Why update_item is rated Medium
An AI agent can call update_item faster than any human can review: one bad instruction and it creates or modifies resources in Grabblist by the hundred, each call as confident as the last.
Risk signalsHigh parameter count (15 properties)
Attacks that exploit this kind of access
The rule that runs update_item safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Grabblist, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For update_item, this is the rule to start with:
update_item 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 Grabblist, apply this rule, and every update_item call is checked against it from then on.
Questions about update_item
Edit fields on one or more saved items. Pass a single id or an array of ids for batch updates (same fields applied to all). For batch: attributes are REPLACED not merged, og:image fallback is skipped. It is categorised as a Write tool in the Grabblist MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
update_item accepts 12 parameters: id, tags, notes, title, images, reason, status, category, quantity, image_url, item_type, attributes. Required: id. The full parameter table on this page comes from the server's own tool schema.
Register the Grabblist MCP server in PolicyLayer and add a rule for update_item: 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 Grabblist. Nothing to install.
update_item 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_item 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_item. 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_item is provided by the Grabblist MCP server (https://mcp.grabbitapp.com/api/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
More on Grabblist, and thousands of servers like it.
This server
Across the catalogue