Log out of TreeBeam by removing stored credentials from the OS keychain.
AI agents call treebeam_logout to retrieve information from TreeBeam MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Even though treebeam_logout only reads data, uncontrolled read access leaks sensitive information and racks up API costs — an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Attacks that exploit this kind of access
Log out of TreeBeam by removing stored credentials from the OS keychain. It is categorised as a Read tool in the TreeBeam MCP Server MCP Server, which means it retrieves data without modifying state.
Register the TreeBeam MCP Server MCP server in PolicyLayer and add a rule for treebeam_logout: 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 TreeBeam MCP Server. Nothing to install.
treebeam_logout is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the treebeam_logout 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 treebeam_logout. 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.
treebeam_logout is provided by the TreeBeam MCP Server MCP server (treebeam-code/tb-mcpserver). 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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