Control SUMO simulation (connect, step, disconnect).
AI agents invoke control_simulation to trigger actions in SUMO-MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool actively controls an external simulation process (connect, step, disconnect). These are side-effectful operations that drive simulation state forward and interact with the SUMO/TraCI runtime. It spans Execute territory as it triggers external operations whose effects depend on arguments.
From the tool's definition Control SUMO simulation (connect, step, disconnect) — triggers external simulation operations including connecting to, stepping through, and disconnecting from a live traffic simulation via TraCI
Documented attack patterns abuse exactly the kind of access control_simulation gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and SUMO-MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for control_simulation:
{
"version": "1",
"default": "deny",
"tools": {
"control_simulation": {
"limits": [
{
"counter": "control_simulation_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
} control_simulation stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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Control SUMO simulation (connect, step, disconnect). It is categorised as a Execute tool in the SUMO-MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the SUMO-MCP Server MCP server in PolicyLayer and add a rule for control_simulation: 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 SUMO-MCP Server. Nothing to install.
control_simulation is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the control_simulation 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 control_simulation. 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.
control_simulation is provided by the SUMO-MCP Server MCP server (xrds76354/sumo-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 10 SUMO-MCP Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
10 SUMO-MCP Server tools catalogued and risk-classified — across an index of 42,500+ MCP servers.