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Show & tell: a false-consensus check for swarm outputs (remote MCP, one command) #4348
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Show and tell
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When several agents in a swarm converge on the same change, that agreement can be thin: same context, same blind spot. One fix is an outside check before a swarm's result is accepted. It's a remote MCP server, so in Claude Code it's one command:
claude mcp add --transport http screwtape https://screwtape.ai/mcp \ --header "Authorization: Bearer $SCREWTAPE_API_KEY"The two tools that matter here:
consensus: pass 2 to 16 agent answers to the same task and get an agreement score plus a false-consensus risk (low, medium or high), with the answers clustered.judge: pass one task, the agent's output and optional evidence, and getpass,failorretrywith reasons.A coordinator instruction you could start from: "Before accepting a result that more than one agent produced, call
consensuswith each agent's answer. If the risk is high, have a fresh agent re-derive it from the evidence."Plain HTTP works too:
POST https://screwtape.ai/v1/consensuswith{"task": "...", "answers": [{"agent_id": "coder-1", "output": "..."}, ...]}.Full runnable example: https://github.com/screwtapeai/screwtape-mcp-clients
Disclosure: I build Screwtape.ai. An independent judge for your agents. Screwtape isn't tied to any agent framework or AI model provider, so its verdicts are never grading their own work. Every verdict comes with a signed receipt you can verify yourself, which gives you a record you can trust when it matters.
judgeandconsensus): https://screwtape.ai/mcpreceiptobject to https://screwtape.ai/v1/receipts/verify.We're looking for a few design partners who run multi-agent swarms in production: 1,000 free calls a day for 60 days, plus direct input on the roadmap. Mostly I'd like to hear where this pattern breaks for you. Reply here or email screwtapedev@gmail.com.
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