AI agents invoke run_simulation to trigger actions in PyPSA 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 a simulation, which is a computational operation that processes the energy system model and produces results. It does not delete or modify the underlying model structure (those are handled by separate tools like delete_model and add_component), but rather runs an analysis whose outcomes depend on input arguments. This qualifies as Execute category.
From the tool's definition Tool name is 'run_simulation' and description states it 'Run a simulation on a PyPSA network.' This triggers computational operations on the network model whose effects depend on the network configuration and simulation parameters provided as arguments.
Documented attack patterns abuse exactly the kind of access run_simulation gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and PyPSA MCP, and nothing reaches the server without passing your rules. This is the rule we recommend for run_simulation:
{
"version": "1",
"default": "deny",
"tools": {
"run_simulation": {
"limits": [
{
"counter": "run_simulation_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}run_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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Run a simulation on a PyPSA network. It is categorised as a Execute tool in the PyPSA MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the PyPSA MCP server in PolicyLayer and add a rule for run_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 PyPSA MCP. Nothing to install.
run_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 run_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 run_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.
run_simulation is provided by the PyPSA MCP server (open-energy-transition/pypsa-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 22 PyPSA MCP tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
22 PyPSA MCP tools catalogued and risk-classified — across an index of 42,500+ MCP servers.