rank_interventions
Rank potential interventions by expected trajectory delta. Each intervention includes expected impact, confidence, cost, timeframe, and what evidence would confirm or deny its effect.
This record as markdown: /tools/io-github-homenshum-nodebench/rank-interventions.md
What rank_interventions does on Nodebench
AI agents call rank_interventions to retrieve information from Nodebench without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why rank_interventions is rated Low
The tool performs analysis and ranking of interventions based on provided data and returns results. This is a read operation—it queries or evaluates information without creating, modifying, deleting, executing code, or processing financial transactions. The confidence is slightly below maximum due to minimal description detail, but the semantic intent is clearly analytical/retrievalrather than action-oriented.
From the tool's definition Tool name 'rank_interventions' and description 'Rank potential interventions by expected trajectory delta' indicates the tool retrieves, analyzes, and returns ranked data about interventions with their attributes (impact, confidence, cost, timeframe,…
Attacks that exploit this kind of access
The rule that runs rank_interventions 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 rank_interventions, this is the rule to start with:
rank_interventions is read-only, so it stays allowed. 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 rank_interventions call is checked against it from then on.
Questions about rank_interventions
Rank potential interventions by expected trajectory delta. Each intervention includes expected impact, confidence, cost, timeframe, and what evidence would confirm or deny its effect. It is categorised as a Read tool in the Nodebench MCP Server, which means it retrieves data without modifying state.
Register the Nodebench MCP server in PolicyLayer and add a rule for rank_interventions: 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.
rank_interventions is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the rank_interventions 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 rank_interventions. 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.
rank_interventions 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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