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Ai Consensus Connector MCP Server

Developer ToolsModerate6.8MCP RegistryLocal
Free

Server data from the Official MCP Registry

Standing review layer for coding agents: Claude, GPT and Gemini debate, return consensus + dissent.

About

Standing review layer for coding agents: Claude, GPT and Gemini debate, return consensus + dissent.

Security Report

6.8
Moderate6.8Moderate Risk

This is a well-structured MCP connector for a legitimate third-party AI service. Authentication is properly required via API key, permissions are narrowly scoped to network API calls only, and code quality is clean with proper input validation. The server makes authenticated HTTPS requests to a hosted service and does not expose dangerous local operations. Minor quality observations around error handling do not materially impact security posture. Supply chain analysis found 1 known vulnerability in dependencies (0 critical, 1 high severity). Package verification found 1 issue.

3 files analyzed · 5 issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

HTTP Network Access

Connects to external APIs or services over the internet.

env_vars

Check that this permission is expected for this type of plugin.

What You'll Need

Set these up before or after installing:

AI Consensus API key (create one at https://ai-consensus.ai/app/api-keys)Required

Environment variable: AI_CONSENSUS_API_KEY

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "io-github-thestevenjohnstone-ai-consensus": {
      "env": {
        "AI_CONSENSUS_API_KEY": "your-ai-consensus-api-key-here"
      },
      "args": [
        "-y",
        "ai-consensus-connector"
      ],
      "command": "npx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

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)

  1. Get an API key: sign in at https://ai-consensus.aiDeveloper API → create a key (copy it; it's shown once).

  2. 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.)

  3. Verify it's connected (free — no run is started):

    "Use the check_ai_consensus_connection tool."

  4. 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.
    

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.

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