simulate_decision_paths
Run Monte Carlo simulation for founder decisions. Generates multiple random paths to visualize possible future outcomes. Shows average payoff, success/failure rates, best/worst paths, and which decisions matter most. Use for: fundraising timing, market entry, build-vs-buy, hiring, pivot analysis.
This record as markdown: /tools/io-github-homenshum-nodebench/simulate-decision-paths.md
What simulate_decision_paths does on Nodebench
AI agents invoke simulate_decision_paths to trigger actions in Nodebench. 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.
Why simulate_decision_paths is rated High
The tool executes a simulation engine with user-supplied decision parameters to produce analytical outputs. While it doesn't modify external state or delete data (ruling out Write/Destructive), and it's not performing financial transactions (not Financial), it does execute algorithmic operations whose results depend on input arguments.
From the tool's definition Tool runs a Monte Carlo simulation, which involves executing computational algorithms that generate multiple random paths and process outcomes.
Attacks that exploit this kind of access
The rule that runs simulate_decision_paths safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Nodebench, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For simulate_decision_paths, this is the rule to start with:
simulate_decision_paths 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Nodebench, apply this rule, and every simulate_decision_paths call is checked against it from then on.
Questions about simulate_decision_paths
Run Monte Carlo simulation for founder decisions. Generates multiple random paths to visualize possible future outcomes. Shows average payoff, success/failure rates, best/worst paths, and which decisions matter most. Use for: fundraising timing, market entry, build-vs-buy, hiring, pivot analysis. It is categorised as a Execute tool in the Nodebench MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Nodebench MCP server in PolicyLayer and add a rule for simulate_decision_paths: 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 Nodebench. Nothing to install.
simulate_decision_paths 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 simulate_decision_paths 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 simulate_decision_paths. 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.
simulate_decision_paths is provided by the Nodebench MCP server (nodebench-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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