What Is an MCP Server and Why Your SaaS Startup Might Need One

Introduction

As AI-driven personalization and automation become foundational in SaaS products, backend infrastructure must evolve to support modular, multi-agent workflows. Enter the MCP: Modular Control Plane server.

An MCP server acts as the orchestrator for distributed AI agents, microservices, and workflows — allowing your SaaS to scale rapidly, manage asynchronous events, and add intelligent behavior without monolithic rewrites.

This guide explains what an MCP server is, what problems it solves, and how to set one up with practical steps.

What You’ll Learn

  • The core architecture of an MCP server

  • How it compares to traditional API gateways and schedulers

  • When you need one for your SaaS

  • Step-by-step: building a basic MCP server

What Is an MCP Server?

An MCP (Modular Control Plane) server is a system-level orchestrator that:

  • Routes tasks between agents or services

  • Maintains state across async workflows

  • Coordinates RAG pipelines, AI decisions, and business logic

  • Provides observability and retry/failure handling

Think of it as a message-aware stateful brain for your backend, often sitting above queues like Redis, Kafka, or nats.io, and below your UI and microservices.

When Do You Need an MCP Server?

Consider adopting an MCP server if:

  • Your product uses multiple AI agents or microservices

  • You require user-level context to persist across tasks

  • You're building workflows with retries, branching, or approval steps

  • You want to decouple logic from UI, DB, or agent endpoints

Use Case Example: AI-Powered CRM Workflow

Problem: A user submits a form. You want to:

  1. Store the user data

  2. Score the lead with an LLM

  3. Route the lead to a sales rep

  4. Send personalized follow-up

With an MCP server, you:

  • Trigger a workflow from a webhook

  • Use MCP to call a series of microservices/agents

  • Monitor status, persist memory, retry on error

Step-by-Step: Build a Basic MCP Server

Step 1: Define Core Architecture

Use a message-passing or task framework:

  • Node.js with BullMQ (Redis-based)

  • Python with Celery (RabbitMQ/Redis)

  • Temporal.io (durable workflows)

Components:

  • Trigger layer (webhooks, events)

  • Task handler (MCP engine)

  • Agent/task registry

  • State manager (Redis, Postgres)

Step 2: Create a Job Dispatcher

In Node.js (BullMQ):

const { Queue } = require('bullmq')
const queue = new Queue('mcp-tasks')

queue.add('score-lead', { userId, answers })

Step 3: Add Agent Handlers

Example: Scoring agent

const { Worker } = require('bullmq')
new Worker('mcp-tasks', async job => {
  if (job.name === 'score-lead') {
    const result = await callOpenAI(job.data.answers)
    await updateCRM(job.data.userId, result)
  }
})

Step 4: Persist State

Use Redis or Postgres to store:

  • Current job status

  • Contextual memory for the user

  • Retry/error logs

Step 5: Monitor and Debug

Build a dashboard or use:

  • Temporal Web UI

  • RedisInsight

  • Custom logs via Supabase or Logflare

Bonus: Add Workflow DSL (Domain-Specific Language)

Once your MCP is stable, define workflows in YAML or JSON:

lead-intake:
  - trigger: webhook
  - tasks:
    - score-lead
    - assign-sales-rep
    - send-follow-up

This opens up UI-based orchestration for non-developers.

Conclusion

An MCP server can significantly improve how you scale AI, workflows, and microservices across your SaaS product. It provides the glue between modular agents and user-facing actions, offering reliability, traceability, and scalability.

If your SaaS is starting to feel like a tangled web of APIs and background jobs, it may be time to implement an MCP.

MCP ServerFrancesca Tabor