Run Monte Carlo simulation on backtest results. Args: symbol: Stock symbol strategy: Strategy type start_date: Start date (YYYY-MM-DD) end_date: End date (YYYY-MM-DD) num_simulations: Number of Monte Carlo simulations Strategy-specific parameters as individual arguments Returns: Monte Carlo simul...
AI agents invoke monte_carlo_simulation to trigger actions in MaverickMCP. 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 Monte Carlo simulation algorithm with parameters supplied by the user. While it is not destructive (no data deletion), not financial in itself (it analyzes/simulates but doesn't move money), and not a simple read operation (it performs computation), it qualifies as Execute because it runs computational code whose outcome depends on the input arguments.
From the tool's definition The tool description states it will "Run Monte Carlo simulation on backtest results." The verb 'Run' combined with the computational operation of executing simulations indicates execution of code/algorithms.
Documented attack patterns abuse exactly the kind of access monte_carlo_simulation gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and MaverickMCP, and nothing reaches the server without passing your rules. This is the rule we recommend for monte_carlo_simulation:
{
"version": "1",
"default": "deny",
"tools": {
"monte_carlo_simulation": {
"limits": [
{
"counter": "monte_carlo_simulation_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}monte_carlo_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.
Free to start. No card required.
Run Monte Carlo simulation on backtest results. Args: symbol: Stock symbol strategy: Strategy type start_date: Start date (YYYY-MM-DD) end_date: End date (YYYY-MM-DD) num_simulations: Number of Monte Carlo simulations Strategy-specific parameters as individual arguments Returns: Monte Carlo simulation results with confidence intervals. It is categorised as a Execute tool in the MaverickMCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Maverick MCP server in PolicyLayer and add a rule for monte_carlo_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 MaverickMCP. Nothing to install.
monte_carlo_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 monte_carlo_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 monte_carlo_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.
monte_carlo_simulation is provided by the Maverick MCP server (wshobson/maverick-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 54 MaverickMCP tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
54 MaverickMCP tools catalogued and risk-classified — across an index of 42,500+ MCP servers.