Launch Gazebo simulation environment via WebSocket server.
AI agents invoke launch_gazebo to trigger actions in ROS MCP. 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 executes an external simulator launch—an operation whose effects cannot be easily predicted or reversed without additional commands. While not immediately destructive to persistent data, it allocates system resources, establishes network connections (WebSocket), and initializes a simulation environment that could affect robot behavior or consume significant computational resources.
From the tool's definition Tool description: 'Launch Gazebo simulation environment via WebSocket server.' The verb 'Launch' indicates triggering an external operation (starting a simulator).
Documented attack patterns abuse exactly the kind of access launch_gazebo gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and ROS MCP, and nothing reaches the server without passing your rules. This is the rule we recommend for launch_gazebo:
{
"version": "1",
"default": "deny",
"tools": {
"launch_gazebo": {
"limits": [
{
"counter": "launch_gazebo_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}launch_gazebo 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.
Free to start. No card required.
Launch Gazebo simulation environment via WebSocket server. It is categorised as a Execute tool in the ROS MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the ROS MCP server in PolicyLayer and add a rule for launch_gazebo: 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 ROS MCP. Nothing to install.
launch_gazebo 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 launch_gazebo 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 launch_gazebo. 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.
launch_gazebo is provided by the ROS MCP server (yutarop/ros-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 24 ROS MCP tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
24 ROS MCP tools catalogued and risk-classified — across an index of 42,500+ MCP servers.