cQnce MCP server

@cqnce/mcp-server

Official MCP server for cQnce — add human-in-the-loop authorization to any AI agent or workflow.

Connect Claude, Cursor, GitHub Copilot, or any MCP-compatible client to cQnce so the AI can request human approval before performing risky or irreversible actions.

Quick start

npx @cqnce/mcp-server

Set the CQNCEAPIKEY environment variable to your project API key before running.

Approval modes

Mode 1 — Agent-requested approval

The agent calls waitforapproval or submitauthorizationrequest itself, as instructed in the system prompt or via tool discovery. This is the easiest integration path and works well for supervised workflows.

Risk: a compromised or misconfigured agent may simply not call the tool.

Mode 2 — Enforced approval gate (recommended for production)

The host application or executor intercepts every sensitive tool call before it runs and requires a prior cQnce approval, regardless of what the agent requested. The gate lives outside the model context — the agent cannot skip it.

This is a real security boundary. Build enterprise and compliance-sensitive integrations on this mode.

# Example: host-side interception (framework-agnostic)
HIGH_RISK_TOOLS = {"send_payment", "deploy_production", "delete_customer"}

def execute_tool_call(tool_call):
    if tool_call.name not in HIGH_RISK_TOOLS:
        return run_tool(tool_call)
    decision = cqnce.submit_and_wait(
        payload={"action": tool_call.name, "parameters": tool_call.arguments},
        timeout_seconds=300,
    )
    if decision["status"] != "APPROVED":
        return {"error": "not_authorized", "status": decision["status"]}
    return run_tool(tool_call)

Add to ~/Library/Application Support/Claude/claudedesktopconfig.json (macOS) or %APPDATA%\Claude\claudedesktopconfig.json (Windows):

{
  "mcpServers": {
    "cqnce": {
      "command": "npx",
      "args": ["-y", "@cqnce/mcp-server"],
      "env": {
        "CQNCE_API_KEY": "your-project-api-key"
      }
    }
  }
}

Cursor

Add to .cursor/mcp.json in your project (or the global ~/.cursor/mcp.json):

{
  "mcpServers": {
    "cqnce": {
      "command": "npx",
      "args": ["-y", "@cqnce/mcp-server"],
      "env": {
        "CQNCE_API_KEY": "your-project-api-key"
      }
    }
  }
}

Any MCP client (stdio transport)

CQNCE_API_KEY=your-project-api-key npx @cqnce/mcp-server

Cloud agents (Streamable HTTP / remote MCP)

For cloud-hosted agents that cannot run local processes, use the cQnce MCP Worker deployed on Cloudflare Workers. It speaks the MCP Streamable HTTP transport and is stateless — every request authenticates with your API key.

Configure your cloud agent framework to connect to:

https://mcp.cqnce.app/mcp
Authorization: Bearer <your-project-api-key>

Example (Claude API with MCP):

import anthropic

client = anthropic.Anthropic()

response = client.beta.messages.create(
    model="claude-opus-4-5",
    max_tokens=1024,
    mcp_servers=[
        {
            "type": "url",
            "url": "https://mcp.cqnce.app/mcp",
            "name": "cqnce",
            "authorization_token": "your-project-api-key",
        }
    ],
    messages=[{"role": "user", "content": "..."}],
    betas=["mcp-client-2025-04-04"],
)

Self-hosting

The Worker source is in this repository. Deploy your own instance to Cloudflare Workers:

npm install
npx wrangler deploy

Set CQNCEBASEURL in the Cloudflare dashboard if you use a private cQnce deployment (defaults to https://api.cqnce.app).

Configuration

Shortened. The full README is on GitHub.