AI agents use update_card to create or update resources in Favro MCP — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Favro MCP environment.
An AI agent can call update_card faster than any human can review — one bad instruction and it creates or modifies resources in Favro MCP by the hundred, each call as confident as the last.
Attacks that exploit this kind of access
update_card. It is categorised as a Write tool in the Favro MCP MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Favro MCP server in PolicyLayer and add a rule for update_card: 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 Favro MCP. Nothing to install.
update_card 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_card 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_card. 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_card is provided by the Favro MCP server (truls27a/favro-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.