run_entity_intelligence_mission
Run a full DeepTrace entity intelligence mission with optional bounded research cell. Unifies relationship mapping, ownership, supply chain, signals, and causal analysis. Pass researchCell=true for threshold-driven re-analysis when the investigation has gaps, or forceResearchCell=true to explicit...
This record as markdown: /tools/io-github-homenshum-nodebench/run-entity-intelligence-mission.md
What run_entity_intelligence_mission does on Nodebench
AI agents invoke run_entity_intelligence_mission 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_entity_intelligence_mission is rated High
This tool executes a complex multi-stage intelligence operation (DeepTrace mission) that performs analysis, mapping, and re-analysis cycles. While the operation itself is not destructive and appears to be information-gathering, it triggers external dependent actions and analysis workflows.
From the tool's definition 'Run a full DeepTrace entity intelligence mission' with 'relationship mapping, ownership, supply chain, signals, and causal analysis' and optional 'bounded research cell' with 're-analysis when the investigation has gaps' — these are triggered operations…
Attacks that exploit this kind of access
The rule that runs run_entity_intelligence_mission 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_entity_intelligence_mission, this is the rule to start with:
run_entity_intelligence_mission 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_entity_intelligence_mission call is checked against it from then on.
Questions about run_entity_intelligence_mission
Run a full DeepTrace entity intelligence mission with optional bounded research cell. Unifies relationship mapping, ownership, supply chain, signals, and causal analysis. Pass researchCell=true for threshold-driven re-analysis when the investigation has gaps, or forceResearchCell=true to explicitly force the bounded cell. 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_entity_intelligence_mission: 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_entity_intelligence_mission 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_entity_intelligence_mission 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_entity_intelligence_mission. 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_entity_intelligence_mission 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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