consensus
AI agents call consensus to retrieve information from OpenClaw Consensus without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Based on context, this tool appears to aggregate or retrieve consensus results from multiple LLMs. The server's stated purpose is 'hallucination mitigation through consensus + disagreement scoring,' implying read-like analysis of LLM outputs.
From the tool's definition Tool named 'consensus' with empty description; sibling tools 'cheapest_route' and 'disagreement_score' suggest data aggregation and scoring operations. No language suggesting write, execute, delete, or financial operations.
Attacks that exploit this kind of access
consensus. It is categorised as a Read tool in the OpenClaw Consensus MCP Server, which means it retrieves data without modifying state.
Register the OpenClaw Consensus MCP server in PolicyLayer and add a rule for consensus: 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 OpenClaw Consensus. Nothing to install.
consensus 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 consensus 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 consensus. 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.
consensus is provided by the OpenClaw Consensus MCP server (MICONNM/openclaw-consensus-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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