This record as markdown: /tools/ruflo/collective-decide.md
What collective-decide does on Ruflo
AI agents invoke collective-decide to trigger actions in Ruflo. 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 collective-decide is rated High
Invoking a collective decision across a multi-agent swarm is an Execute-class operation: it triggers external operations whose effects depend on arguments and can cascade across many autonomous agents. The blast radius is high because a misused decision request could coordinate harmful or unintended actions across the entire swarm.
From the tool's definition 'Request collective decision from agents' — triggers coordinated action across multiple autonomous agents in a swarm
Attacks that exploit this kind of access
The rule that runs collective-decide safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Ruflo, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For collective-decide, this is the rule to start with:
collective-decide 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 Ruflo, apply this rule, and every collective-decide call is checked against it from then on.
Questions about collective-decide
Request collective decision from agents. It is categorised as a Execute tool in the Ruflo MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Ruflo MCP server in PolicyLayer and add a rule for collective-decide: 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 Ruflo. Nothing to install.
collective-decide 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 collective-decide 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 collective-decide. 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.
collective-decide is provided by the Ruflo MCP server (ruvnet/ruflo). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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