Energy consumption trends — Wh/mi over time.
AI agents call tesla_efficiency to retrieve information from Mcp Teslamate Fleet without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves historical energy consumption data (Wh/mi trends over time) from TeslaMate analytics. It is a read-only query with no side effects on the vehicle or any systems.
From the tool's definition Energy consumption trends — Wh/mi over time
Attacks that exploit this kind of access
Energy consumption trends — Wh/mi over time. It is categorised as a Read tool in the Mcp Teslamate Fleet MCP Server, which means it retrieves data without modifying state.
Register the Mcp Teslamate Fleet MCP server in PolicyLayer and add a rule for tesla_efficiency: 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 Mcp Teslamate Fleet. Nothing to install.
tesla_efficiency 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 tesla_efficiency 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 tesla_efficiency. 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.
tesla_efficiency is provided by the Mcp Teslamate Fleet MCP server (lodordev/mcp-teslamate-fleet). 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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