AI agents use create_work_item to create or update resources in Ado — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Ado environment.
Creating a work item is a reversible write operation—it adds data to the system but can be deleted or modified later. It does not execute code, delete data irreversibly, or move money. The empty description reduces confidence slightly, but the tool name is unambiguous.
From the tool's definition Tool name is 'create_work_item' with no description provided. The name indicates creation of a work item (e.g., task, bug, user story) in Azure DevOps, which is a reversible write operation.
Attacks that exploit this kind of access
create_work_item. It is categorised as a Write tool in the Ado MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Ado MCP server in PolicyLayer and add a rule for create_work_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 Ado. Nothing to install.
create_work_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 create_work_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 create_work_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.
create_work_item is provided by the Ado MCP server (raboley/ado-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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