Should You Use a Gateway Vendor or Build Directly on MCP?

MCP is the protocol, but the Unified Context Layer is what makes it production-ready.

As AI agents shift from novelty to utility, a common infrastructure question arises for product and engineering teams: should we build directly on top of the Model Context Protocol (MCP), or use a gateway vendor to handle integrations and authentication? This post breaks down the tradeoffs and introduces a third path that balances flexibility, scalability and speed: the Unified Context Layer (UCL).

1. Build directly on MCP

MCP is an open protocol that standardises how AI agents describe actions. It defines the structure, not the infrastructure. If you take this route, you will need to handle:

• •Authentication and token management for every integration
• •Routing logic for incoming commands
• •Tenant isolation and context detection
• •Retries, error handling and permissions
• •Scalable infrastructure for production environments

This approach offers full control and customisation, but it is complex and resource-intensive, especially when dealing with multitenant SaaS or enterprise-grade applications.

2. Use a gateway vendor

Gateway vendors are ideal for early-stage prototyping or validating integration concepts. Platforms such as Composio, Smithery and Pipedream offer prebuilt connectors and simplified OAuth flows to help you quickly integrate third-party APIs. They are not designed for production-scale, AI-native use cases.

• •They focus on authentication, not full action execution.
• •They lack multi-tenant context detection, making user-level routing complex.
• •They impose opinionated workflows, limiting control over how actions are processed.
• •They are hard to extend or deeply customise for advanced needs.
• •They introduce vendor lock-in, which can slow you down as your product evolves.

You will still be writing glue code and workarounds to reach production standards.

3. The UCL advantage

UCL is a managed platform that turns MCP into a secure, scalable system ready for production. It does everything a gateway can, and more:

• •Full compatibility with MCP commands
• •Secure, tenant-aware execution with automatic context detection
• •Deep integration with tools such as Slack, Jira, Notion, Gmail and Microsoft Teams
• •Built-in retries, logging, monitoring and error handling
• •Tenant-based permissions and workspace-level access control
• •Real-time observability and tenant-specific dashboards

Rather than being a connector library or an auth wrapper, UCL is the infrastructure layer you would otherwise have to build from scratch to make MCP work in real environments.

A real-world scenario

Imagine you are building an AI-powered reminder feature inside your product. Different customers want reminders delivered through different channels: customer A prefers Slack, customer B uses Microsoft Teams, and customer C wants an email via Gmail.

• •Using MCP directly: you handle OAuth, token storage, routing and isolation for Slack, build and maintain separate Teams integration logic, and manage Gmail auth and context switching manually.
• •Using a gateway vendor: auth is simplified, but tenant logic and routing are your responsibility, workflow customisation may be limited, isolation is still manual, and there is a risk of token leakage or logic errors without built-in context awareness.
• •Using UCL: you send a single MCP action. UCL auto-routes and executes via Slack with the correct context, detects the tenant and applies permissions before posting to Teams, and uses a secure Gmail setup per user to send the reminder email automatically.

Why this matters

AI agents only create value when they can take secure, contextual, real-world actions. That requires:

• •Multitenancy with strong tenant isolation
• •Fine-grained permissions across users and workspaces
• •Unified monitoring and observability
• •Robust handling of failures and retries
• •Scalable infrastructure for real-time execution

MCP gives you the standard. UCL gives you the engine that runs it safely, reliably and at scale. No extra logic, no routing code, no duplication across tenants.

Moving forward

If you are a SaaS or enterprise team building AI-powered products, you do not have to choose between DIY infrastructure and limited gateway platforms. UCL is the third path. It makes MCP production ready, giving you the flexibility to build powerful agent features without getting bogged down in infrastructure.