🏭 Industrial MCP

AI Control Plane for Operational and B2B Analytics — connecting an organizational knowledge graph, web analytics, and Cloud SQL to Claude.ai via the Model Context Protocol.

A production Model Context Protocol server that lets sales, marketing, and ops teams drive cross-database queries through natural language in Claude.ai instead of switching between dashboards, CLIs, and SQL clients.

The server unifies two data layers behind one agent-addressable surface:

• Operational layer (Neo4j): organizational structure, capability paths, knowledge-graph relationships

• Analytics layer (Cloud SQL / Matomo): visitor analytics, conversion metrics, content performance, B2B company intelligence

Built with Next.js on Vercel, secured via OAuth 2.1 with Auth0, and integrated with Claude.ai through the Connectors marketplace.

🚀 Live Deployment

• Production: https://industrial-mcp-delta.vercel.app

• MCP Endpoint: https://industrial-mcp-delta.vercel.app/api/mcp

• Health Check: https://industrial-mcp-delta.vercel.app/api/health

Connect from Claude.ai → Settings → Connectors → Add custom connector → paste the MCP endpoint.

Related MCP server: MCP Observer Server

🏗️ Architecture

🔐 Authentication & Authorization

The server supports two authentication methods:

• OAuth 2.1 Bearer tokens — issued by Auth0 for users connecting via the Claude.ai Connectors marketplace

• API keys — for programmatic clients

Discovery methods (initialize, tools/list) are unauthenticated. All tool invocations (tools/call) require valid credentials. Each call passes through authenticateRequest for token validation and hasToolPermission for tier-based tool gating — unauthorized callers receive RFC-compliant WWW-Authenticate headers and JSON-RPC -32001 / -32003 error codes.

🛠️ Tools (18)

Visitor Analytics & B2B Intelligence (Matomo)

• get_visitor_analytics — traffic patterns and user behavior

• get_company_intelligence — B2B company data from enriched session data

• get_content_performance — page views, bounce rates, engagement

• get_conversion_metrics — goal tracking and funnel analysis

• query_matomo_database — parameterized Matomo SQL with injection prevention

Knowledge Graph (Neo4j)

• query_knowledge_graph — parameterized Cypher with injection prevention

• find_capability_paths — capability paths and skill networks across the org

• get_organizational_structure — departments and reporting hierarchies

• get_knowledge_graph_stats — node/relationship counts and graph health

Cloud SQL Operations

• explore_database — list tables, inspect schemas, sample data

• query_database — custom SQL with automatic validation (SELECT-only)

• analyze_data — summary, trend, and distribution analysis on tables

• get_cloud_sql_info — system configuration and connection info

• get_cloud_sql_status — connection health and status

Cross-Database

• get_unified_dashboard_data — combined Neo4j + MySQL metrics in a single response

• correlate_operational_relationships — correlate org/operational data with web analytics

Admin

• get_usage_analytics — API key statistics and usage (admin-only)

Utility

• echo — protocol-level testing

💻 Local Development

Prerequisites

• Node.js 18+

• npm or yarn

Setup

git clone https://github.com/intecrel/industrial-mcp.git
cd industrial-mcp
npm install
cp .env.example .env.local   # configure Auth0, Upstash, Cloud SQL, Neo4j, Matomo
npm run dev

Test the MCP endpoint

curl -X POST http://localhost:3000/api/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "initialize",
    "params": {
      "protocolVersion": "2025-06-18",
      "capabilities": {"roots": {"listChanged": false}},
      "clientInfo": {"name": "test-client", "version": "1.0.0"}
    }
  }'

Test with MCP Inspector

npm install -g @modelcontextprotocol/inspector
mcp-inspector http://localhost:3000/api/mcp

🚢 Deployment

Deployed on Vercel as serverless functions:

vercel --prod

Required environment variables: Auth0 credentials (issuer, audience, client config), Upstash Redis URL, Cloud SQL connection string, Neo4j connection string, Matomo API token. See .env.example for the full list.

👥 Authors & History

• Sam Durso — Original web-based implementation

• Muhammad Akbar Khawaja (@akbarkhawaja) — Migration from web app to MCP server, OAuth 2.1 / Auth0 integration, knowledge-graph and Cloud SQL tooling, Claude.ai Connectors integration, current maintainer

• intecrel — Operating organization

The project began as a web-based operational dashboard. Once the client moved to Claude.ai as the primary user-facing surface, it was rebuilt as an MCP server with OAuth-secured tool access and the current 18-tool topology.

📄 License

MIT — see LICENSE.

🙏 Acknowledgments

• Model Context Protocol — protocol specification

• Vercel MCP Adapter

• Anthropic — Claude.ai and MCP development