Ask several frontier models the same question and get one synthesized consensus answer with a confidence score and points of dissent. Preset councils (fast/balanced/deep) or a custom set of models. Dynamic price, quoted in the 402.
AI agents invoke ai.council to trigger actions in Mcp. 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.
This tool executes queries against multiple external AI models and synthesizes their responses, which constitutes triggering external operations. While it has a financial cost component (pay-per-call in USDC), the tool itself is an execution of AI inference rather than a direct financial transaction like a payment or trade.
From the tool's definition 'Ask several frontier models the same question' and 'get one synthesized consensus answer' — triggers external LLM API calls across multiple frontier models and synthesizes results; 'Dynamic price, quoted in the 402' indicates per-call execution with real…
Attacks that exploit this kind of access
Ask several frontier models the same question and get one synthesized consensus answer with a confidence score and points of dissent. Preset councils (fast/balanced/deep) or a custom set of models. Dynamic price, quoted in the 402. It is categorised as a Execute tool in the Mcp MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the MCP server in PolicyLayer and add a rule for ai.council: 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 Mcp. Nothing to install.
ai.council 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 ai.council 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 ai.council. 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.
ai.council is provided by the MCP server (@2sio/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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