The Model Context Protocol (MCP) has been called the "USB-C for AI agents," and it's gaining momentum as a way to standardize how agents invoke tools. But here's the real question for developers building B2B SaaS: does a standardized protocol actually deliver the secure, reliable, multi-tenant integrations your customers need?

Short answer: not on its own.

• MCP standardizes how agents discover and invoke tools (a common "tool-calling" protocol).

• MCP does not provide production-integration essentials such as multi-tenant OAuth/token storage, retries/idempotency, observability, audit logs, or policy/RBAC.

• MCP is best for discrete agent-triggered actions, not continuous data syncs (RAG ingestion) or event-driven workflows.

• To ship enterprise-ready agent integrations, you need MCP + an integration platform layer to handle auth, reliability, and governance.

Look, MCP is a promising protocol for standardizing how an agent calls a tool. But it's not a complete integration platform. You'll still need to build systems for multi-tenant authentication, production-grade reliability, and enterprise security. That's a lot of work.

What is the model context protocol (MCP)?

MCP is an open-source protocol that defines how an AI agent (the client) discovers and executes tools exposed by an application (the server). The agent asks a server, "What tools do you have?" and then follows up with, "Run this specific tool with these inputs."

The interaction builds on three core primitives:

• Tools: The actions an agent can execute, like create_ticket or find_user.

• Prompts: Pre-defined instructions or context that guide the agent's behavior.

• Resources: Data sources, like files or databases, that the agent can access.

The "USB-C" comparison is actually pretty helpful here. MCP standardizes the connection and the language spoken over the wire. Any agent or tool that speaks MCP can communicate without the need for custom adapters. But the protocol doesn't manage what happens on either side of that connection. It doesn't handle credential storage, retries, or audit logs.

What MCP standardizes: tool discovery and invocation

MCP's real strength? Creating a standard, predictable language for agents to interact with tools.

Before MCP, developers wrote custom wrappers for every single API an agent needed. That approach was brittle and didn't scale. MCP cuts through this complexity. An agent doesn't need to learn the quirks of a hundred different REST APIs. It just needs to speak MCP.

Here's what that looks like in practice:

// 1. Discover available tools from the server
const tools = await mcpClient.listTools();
// Example Output: [{ "name": "create_ticket", "description": "Creates a new support ticket." }]

// 2. Execute a specific tool with parameters
const result = await mcpClient.runTool('create_ticket', {
  title: 'API access issue',
  priority: 'High'
});

Simple. Standardized. That's MCP's core value.

Why MCP alone isn't enough for production-ready integrations

MCP defines what happens (the tool call). But a production system needs to handle how it happens: the secure, reliable execution of that call.

The MCP spec intentionally leaves out the complex parts. And honestly, those are the parts that eat up engineering time.

Why does MCP not solve multi-tenant authentication

MCP mentions authentication but doesn't specify how to implement the tricky bits. In a multi-tenant B2B app, you're managing thousands, maybe millions, of customer credentials.

That means building a system to store encrypted user tokens. It means handling OAuth 2.0 refresh cycles for hundreds of different APIs. It means enforcing strict tenant boundaries so one user's agent never accidentally touches another user's data.

None of that is in MCP's scope.

What you still need for reliability and observability in production

Real-world APIs go down. They return weird errors. They rate-limit you.

A production system needs resilience. You'll need idempotency keys to prevent duplicate records from being created (e.g., charging a customer twice). You'll need automated retry logic with exponential backoff to handle temporary outages gracefully.

MCP doesn't cover any of this. And you'll also need observability to monitor integration health, track error rates per customer, and alert your team when something breaks.

What enterprises require beyond MCP: security and governance

Authentication is just step one. Enterprises need more.

They need immutable audit trails of every action an agent performs on behalf of a user. That's critical for compliance standards like SOC 2. They need policy enforcement to block destructive operations (like an agent calling deleteAllUsers). They need role-based access controls so agents can only perform actions allowed by their user's role.

You're building all of that yourself.

Why MCP isn't designed for data sync and event-driven workflows

Agents are powerful, but they're only one piece of the integration puzzle.

To power a Retrieval-Augmented Generation (RAG) system, you need high-volume, continuous data syncs keeping your vector database current. You also need event-driven workflows, like automatically creating an invoice when a Salesforce deal hits "Closed-Won."

MCP handles discrete, agent-triggered tool calls. Not these other patterns.

How to move from MCP to a production integration platform

Multi-tenant auth, reliability, enterprise security, diverse workflow patterns. All outside MCP's scope.

Solving these problems takes more than a protocol. You need an integration platform layer.

This layer sits between your AI agent and the end-application APIs. It handles the hard, undifferentiated work. Your agent makes a tool call (via MCP or your preferred method), and the platform handles everything beneath it.

Instead of spending months building a secure token vault, implementing retry logic for dozens of APIs, and creating audit logs, you get those capabilities out of the box. Think of it as "MCP plus an integration platform" to hit production readiness.

How Composio makes MCP production-ready

Composio isn't an alternative to MCP. It's the integration platform that fills the gaps.

We handle the complexity so you can ship agentic features securely. Managed authentication that handles multi-tenant token complexity. Reliability patterns like automated retries and rate limit handling. Enterprise-grade security controls.

Your team focuses on your core product. We make sure the integration layer is secure, reliable, and ready for enterprise customers.

Key takeaway: MCP is a protocol, not a production integration platform

MCP is a valuable protocol that standardizes the syntax of tool calls. That's a real step forward for the AI ecosystem.

But it's not a platform. To ship agentic features to real customers, you need something that handles multi-tenant auth, security, reliability, and governance.

Build on a solid integration foundation first. Then you're ready for any agent framework or protocol that comes next, including MCP.

Frequently Asked Questions

Is MCP a replacement for OpenAPI?

No, they're complementary. The OpenAPI specification defines the structure of a RESTful API. MCP specifies how an agent discovers and executes tools.

How does MCP handle authentication in production?

The spec is flexible, suggesting patterns like OAuth but providing no implementation. You build secure credential storage, token refreshing, and multi-tenant permission management yourself.

MCP vs LangChain tools: what's the difference?

LangChain Tools are a software abstraction within a specific Python/JS framework. MCP is an open, framework-agnostic protocol. You could build a LangChain tool that talks to an MCP server.

Can MCP be used for RAG data sync?

Not really. MCP handles discrete, agent-triggered actions (tool calls), not the continuous, high-volume data synchronization you need for a RAG vector database.

Is MCP secure enough for enterprise use?

The protocol itself isn't inherently insecure. But it leaves critical security components like multi-tenant auth, audit logging, and policy enforcement entirely up to you.

Is MCP enough to build production-ready AI agent integrations?

No. MCP standardizes tool discovery and invocation. You still need separate systems for multi-tenant auth, reliability (retries/idempotency), observability, and enterprise governance.

What problems does MCP solve vs. what falls outside its scope?

MCP provides a standard way for agents to discover and use tools. It doesn't handle token storage, OAuth refresh, audit logs, policy enforcement, or data sync/event pipelines.

Do I need MCP to build an AI agent that uses tools?

No. You can use framework-specific abstractions or direct API calls. But MCP helps when you want a standardized, interoperable protocol across tools and agents.

How does MCP differ from OpenAI function calling (tool calling)?

OpenAI tool calling is a model/API feature for producing structured tool-call arguments. MCP is a protocol for discovering tools and executing them against an MCP server.

How do you implement multi-tenant OAuth with MCP?

You don't, at least not with MCP alone. You build or use a platform that securely stores per-tenant tokens, refreshes them, and enforces tenant isolation and permissions.

Can MCP handle retries, rate limits, and idempotency automatically?

No. You implement those reliability patterns in your integration layer or middleware, not in MCP itself.

Can I convert my existing OpenAPI (Swagger) specs to MCP?

Yes, it is possible to map OpenAPI endpoints to MCP tools, but it is not automatic. You typically need to write a translation layer or use a middleware to parse the OpenAPI spec and expose the endpoints as MCP-compatible tools. This is one area where integration platforms help by automating the conversion.

What is the operational overhead of self-hosting MCP servers?

Self-hosting MCP servers introduces significant maintenance. You are responsible for the server's uptime, securing the transport layer (SSE/Stdio), managing connection limits, and handling updates whenever the underlying APIs change. For production environments, this often requires a dedicated DevOps effort similar to maintaining microservices.