AI Consensus Connector
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@AI Consensus ConnectorShould I use PostgreSQL or MongoDB for the user data schema?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AI Consensus — coding-agent connector
Make AI Consensus a standing part of your development model. Your coding agent — Claude Code, Cursor, Cline, any MCP client — routes every decision of consequence through three frontier AIs (Claude, GPT, Gemini) that answer independently, then cross-examine each other, and return one recommendation plus the strongest dissent. You keep working inside your agent; the panel reviews as you go.
This is not a tool you reach for occasionally. It's a review layer you switch on once, and from then on everything that matters gets debated by three independent models before it ships. (Honest bill: a run takes a few minutes and, on the bring-your-own-keys plan, costs roughly $0.40–$1 of your own provider tokens.)
Setup — Claude Code (about 2 minutes)
Get an API key: sign in at https://ai-consensus.ai → Developer API → create a key (copy it; it's shown once).
Add the connector:
claude mcp add ai-consensus --scope user \ --env AI_CONSENSUS_API_KEY=aic_live_your_key_here \ -- npx -y ai-consensus-connector(Self-hosting? add
--env AI_CONSENSUS_API_URL=https://your-instance.)Verify it's connected (free — no run is started):
"Use the check_ai_consensus_connection tool."
Make it your standing rule — add to your project's
CLAUDE.md:STANDING RULE: This project develops with AI Consensus as its review layer. Route every decision of consequence — architecture, design, implementation approach, tricky bugs, anything whose cost of being wrong is real — through consult_ai_consensus as a matter of course, and weigh its recommendation and strongest dissent before acting. Purely mechanical edits (formatting, typos, exact instructed changes) don't need a run. When in doubt, consult.Teams that want everything routed can use the strict variant instead:
STRICT MODE: Consult consult_ai_consensus before every non-trivial change of any kind, and weigh the dissent before acting.
Related MCP server: tenth-man-mcp
Setup — Cursor / Cline / other MCP clients
Add a stdio MCP server with command npx -y ai-consensus-connector and the
AI_CONSENSUS_API_KEY environment variable. For Cursor, in mcp.json:
{
"mcpServers": {
"ai-consensus": {
"command": "npx",
"args": ["-y", "ai-consensus-connector"],
"env": { "AI_CONSENSUS_API_KEY": "aic_live_your_key_here" }
}
}
}Then put the standing rule in your project's agent rules file (.cursorrules,
.clinerules, etc.).
Tools
consult_ai_consensus— route a decision/task through the panel and wait for the result (a few minutes).start_ai_consensus/get_ai_consensus_result— fire-and-forget + collect later.check_ai_consensus_connection— zero-cost setup check (reachability, key accepted, provider keys present).cancel_ai_consensus_run— stop a run.
Your key authenticates to your account and bills your plan (unlimited on your
own keys, or prepaid credits). Keep it secret — and note that pasting the
--env form above stores the key in your shell history; use your client's
config file if that concerns you.
Open source, hosted service
This connector is open source under the MIT license. It is a thin client for the paid, hosted AI Consensus service — you bring your own AI Consensus API key; the deliberation engine itself runs on our servers and is not part of this repository. The MIT license covers this connector's code only and grants no rights to the AI Consensus name or branding.
Support boundary: connector bugs and setup issues → GitHub issues; account, API-key or billing questions → support@ai-consensus.ai.
This server cannot be deployed
Maintenance
Related MCP Connectors
Multi-LLM council: 25+ frontier models in parallel, consensus scoring, verdict-first code review.
Guardian agent for AI coding: four frontier models review risky diffs and commits before they ship.
Trust gate for AI agents: multi-model adversarial consensus, signed and verifiable verdicts.
Multi-reviewer AI code review: surfaces findings the reviewers agree on, disputes flagged.
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