run_deep_sim
Run a multi-agent scenario simulation with bounded branching and budget controls. Instantiates agents with personas and incentives, varies conditions across branches, and generates an analytical report with convergence metrics.
This record as markdown: /tools/io-github-homenshum-nodebench/run-deep-sim.md
What run_deep_sim does on Nodebench
AI agents invoke run_deep_sim 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 run_deep_sim is rated High
This tool executes multi-agent simulations with configurable parameters (personas, incentives, conditions, branching). The outcome depends entirely on the simulation logic and input arguments provided by the user. This is a compute-intensive operation with side effects (resource consumption, report generation) that goes beyond simple data retrieval.
From the tool's definition 'Run a multi-agent scenario simulation' and 'Instantiates agents' indicate dynamic execution of external processes.
Attacks that exploit this kind of access
The rule that runs run_deep_sim 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 run_deep_sim, this is the rule to start with:
run_deep_sim 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 run_deep_sim call is checked against it from then on.
Questions about run_deep_sim
Run a multi-agent scenario simulation with bounded branching and budget controls. Instantiates agents with personas and incentives, varies conditions across branches, and generates an analytical report with convergence metrics. 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 run_deep_sim: 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.
run_deep_sim 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_deep_sim 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_deep_sim. 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_deep_sim 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.
More on Nodebench, and thousands of servers like it.
Across the catalogue