scorton-mcp

Cloudflare Workers MCP server for behavioral cybersecurity and AI risk in financial workflows.

Built with the universal @modelcontextprotocol/sdk and Cloudflare's agents McpAgent runtime.

What this server provides

MCP tools

• score_human_risk
• score_ai_risk
• score_workflow_risk
• run_ai_eval_summary
• generate_audit_snapshot
• health_check

MCP resources

• scorton://policy-profile — active scoring policy and thresholds.

All tool inputs are validated with Zod and outputs are machine-oriented JSON.

Architecture

src/
  config/        # app metadata + scoring thresholds
  mcp/           # ScortonMCP agent (tools + resources)
  schemas/       # Zod input/output schemas
  services/      # pure business logic / scoring
  utils/         # logger + structured errors
tests/           # node:test unit tests

Runtime

• Framework: @modelcontextprotocol/sdk + agents/mcp
• Hosting: Cloudflare Workers + Durable Objects
• Endpoint: https://<worker>.workers.dev/mcp

Quick start

Prerequisites

• Node.js 18+
• Cloudflare account with Workers enabled
• wrangler login

Local development

npm install
npm run dev

Server runs at http://localhost:8788/mcp.

Test with MCP Inspector:

npx @modelcontextprotocol/inspector@latest
# Open http://localhost:5173 and connect to http://localhost:8788/mcp

Deploy to Cloudflare

1. Update wrangler.jsonc vars:
   • MCP_URL → your worker URL (e.g. https://scorton-mcp.scortonlabs.workers.dev)
   • TEAM_DOMAIN → your Access team (e.g. https://scortonlabs.cloudflareaccess.com)
   • POLICY_AUD → AUD tag from Zero Trust → Access → your application
2. Deploy:

npm run deploy

3. Connect clients to https://scorton-mcp.scortonlabs.workers.dev/mcp.

Cloudflare Access (required for protected workers)

If the worker is behind a self-hosted Access application, set TEAM_DOMAIN and POLICY_AUD in wrangler.jsonc (or Worker dashboard vars). The worker validates the Cf-Access-Jwt-Assertion header on /mcp requests.

Find the AUD tag: Zero Trust → Access → Applications → your app → AUD tag.

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "scorton": {
      "command": "npx",
      "args": ["mcp-remote", "https://scorton-mcp.<your-subdomain>.workers.dev/mcp"]
    }
  }
}

Cursor — add as a remote MCP server with the same URL.

Configuration

Scoring thresholds are set via Worker vars in wrangler.jsonc:

Variable	Default	Purpose
RISK_HUMAN_BASELINE_THRESHOLD	0.2	Human baseline deviation flag threshold
RISK_AI_FAILURE_WEIGHT	0.5	Weight of eval failures in AI risk score
RISK_WORKFLOW_HIGH_VALUE_THRESHOLD	100000	USD threshold for high-value workflow flag

Scripts

npm run dev        # wrangler dev (local Worker)
npm run deploy     # deploy to Cloudflare
npm run typecheck  # TypeScript checks
npm run test       # unit tests (services layer)
npm run cf-typegen # regenerate worker Env types

Example tool payloads

score_human_risk

{
  "actorId": "user-42",
  "baselineDeviation": 0.61,
  "velocityAnomalyScore": 0.88,
  "navigationAnomalyScore": 0.74,
  "repeatedEditsCount": 7,
  "sourceChannel": "new-device",
  "workflowContext": "wire-transfer-approval"
}

score_ai_risk

{
  "modelId": "llm-payments-v3",
  "evalFailureRate": 0.31,
  "useCaseCriticality": "mission-critical",
  "complianceCategory": "aml",
  "productionExposure": "full",
  "recentIncidents": 2
}

score_workflow_risk

{
  "workflowId": "ap-invoice-2026-0009",
  "sequenceAnomalyScore": 0.67,
  "actorRole": "approver",
  "transactionValueUsd": 240000,
  "vendorOrPaymentChangeSignal": true,
  "changeWindowMinutes": 22
}

Optional: Cloudflare Access MCP portal

Register this server in Cloudflare Access AI controls to add OAuth governance and a managed portal for your team.

Migration from v1 (mcp-use / Dokploy)

v1 used mcp-use on Node.js + Docker. v2 runs on Cloudflare Workers with the standard MCP SDK. Tool names, schemas, and scoring logic are unchanged — only the transport/runtime changed.