DEV Community: Bhavy Shekhaliya

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Bhavy Shekhaliya — Wed, 29 Jul 2026 10:20:54 +0000

Bhavy Shekhaliya

Jul 29

Stop Building Custom AI Integrations. Use MCP Instead.

#mcp

4 min read

Stop Building Custom AI Integrations. Use MCP Instead.

Bhavy Shekhaliya — Wed, 29 Jul 2026 10:20:03 +0000

AI agents are becoming part of everyday software.

Customers want to ask ChatGPT to create tickets, update records, retrieve reports, trigger workflows, and interact with SaaS products using natural language.

For many teams, the first instinct is simple:

"Let's build a custom AI integration."

A few weeks later, the reality starts to look different.

You need to support multiple AI platforms.

You need authentication.

You need tool definitions.

You need documentation.

You need versioning.

You need monitoring.

You need to maintain everything as APIs evolve.

What started as a small integration suddenly becomes another platform your team has to maintain.

After helping teams expose APIs to AI systems, I've seen the same pattern repeatedly:

The challenge isn't connecting one AI model. The challenge is supporting an ecosystem of AI clients.

That's exactly why MCP exists.

The Problem With Custom AI Integrations

Imagine you run a SaaS product with a REST API.

Your customers ask:

Can ChatGPT create records?
Can Claude access our data?
Can Cursor trigger actions?
Can AI agents automate workflows?

A common solution is building a custom integration for each platform.

The result often looks like this:

Custom ChatGPT integration
Custom Claude integration
Custom internal agent integration
Custom documentation
Custom authentication flow
Custom maintenance process

Every new AI platform introduces additional work.

Instead of maintaining one API, you're maintaining multiple AI-specific layers.

APIs Were Built for Applications, Not AI Agents

REST APIs were designed for developers.

Developers can:

Read documentation
Understand request formats
Handle authentication
Manage errors
Combine multiple endpoints

AI agents operate differently.

They need structured descriptions of available actions.

They need clear tool definitions.

They need a consistent way to discover capabilities.

They need context about when and how actions should be used.

Without that layer, every AI integration becomes a custom project.

Enter MCP

MCP (Model Context Protocol) provides a standard way for AI systems to interact with software.

Instead of creating a separate integration for every AI platform, you expose capabilities through a common protocol.

Think of it this way:

REST API = Designed for developers
MCP = Designed for AI agents

Your API remains the source of truth.

MCP becomes the layer that makes those capabilities understandable and usable for AI systems.

Why More SaaS Companies Are Launching MCP Servers

The shift is similar to what happened with APIs years ago.

At one point, companies built custom integrations for every partner.

Eventually APIs became the standard.

Today we're seeing a similar transition with AI.

Instead of building custom AI connections repeatedly, companies are creating MCP servers that work across multiple AI tools.

This provides:

Better interoperability
Faster adoption
Lower maintenance costs
Easier onboarding for customers
Consistent AI experiences

The Hidden Cost Nobody Talks About

Most discussions focus on implementation.

Few teams discuss maintenance.

Let's say your API changes.

You now need to update:

Documentation
Tool descriptions
Integrations
Authentication logic
AI-specific configurations

As your product grows, maintenance becomes the biggest expense.

The more custom integrations you build, the larger that burden becomes.

A standard approach reduces that complexity.

Where OpenAPI Fits In

Many SaaS companies already maintain OpenAPI specifications.

Those specifications already describe:

Endpoints
Parameters
Request schemas
Response schemas
Authentication requirements

That information is extremely valuable.

Instead of recreating everything for AI systems, it can be used as the foundation for an MCP server.

This allows existing API investments to continue delivering value in the AI era.

How We Solved This at 0mcp

While working with API-driven products, we noticed teams repeatedly facing the same problem:

They already had APIs.

They already had documentation.

They already had OpenAPI specifications.

But turning those assets into production-ready MCP servers required significant effort.

That's why we built 0mcp.

Instead of building custom AI integrations from scratch, teams can import an OpenAPI specification, choose which operations should become AI tools, and deploy an MCP endpoint.

The goal isn't replacing APIs.

The goal is making existing APIs accessible to AI systems through a standard interface.

If you're exploring MCP, these resources may help:

Home: https://0mcp.io
Documentation: https://docs.0mcp.io
OpenAPI to MCP: https://0mcp.io
Getting Started: https://docs.0mcp.io

What This Means for SaaS Teams

The question is no longer:

"Should we support AI?"

Most companies already know the answer is yes.

The better question is:

"How do we support AI without creating years of integration debt?"

For many teams, the answer won't be another custom integration.

It will be adopting standards that allow AI systems to interact with software in a consistent way.

That's where MCP is heading.

And just like APIs became a requirement for modern software, MCP is rapidly becoming part of the foundation for AI-ready products.

Final Thoughts

Custom AI integrations seem fast at the beginning.

But every new platform increases complexity.

Every new tool increases maintenance.

Every new workflow creates another system to support.

Standards exist for a reason.

If your product already has an API, the next step may not be building another custom integration.

It may be making that API accessible through MCP.

The companies that solve this early will be much better positioned as AI agents become a standard part of how users interact with software.

REST vs. GraphQL: The Future of API Development

Bhavy Shekhaliya — Mon, 17 Jun 2024 10:11:15 +0000

APIs (Application Programming Interfaces) are the backbone of modern web development, enabling communication between different software systems. Two of the most popular paradigms for building APIs are REST (Representational State Transfer) and GraphQL. Understanding the differences between these two approaches can help developers choose the best tool for their projects.

Understanding REST:

- REST Overview :
┍ REST is an architectural style for designing networked applications. It relies on a stateless, client-server communication model and uses standard HTTP methods such as GET, POST, PUT, DELETE, and PATCH to perform CRUD (Create, Read, Update, Delete) operations.

- Key Characteristics of REST:

Statelessness: Each request from a client to a server must contain all the information needed to understand and process the request. The server does not store any client context between requests.
Scalability: RESTful services can handle a large number of requests and scale horizontally by distributing them across multiple servers.
Uniform Interface: REST APIs have a uniform interface, simplifying and decoupling the architecture. Resources are identified by URLs, and actions are performed using standard HTTP methods.
Caching: Responses from the server can be cached to improve performance and reduce the load on the server.

Understanding GraphQL:

- GraphQL Overview:
┍ GraphQL, developed by Facebook in 2012 and released publicly in 2015, is a query language for APIs and a runtime for executing those queries. It provides a more flexible and efficient approach to data fetching compared to REST.

- Key Characteristics of GraphQL:

Client-Specified Queries: Clients specify exactly what data they need, avoiding over-fetching and under-fetching issues.
Single Endpoint: All queries are sent to a single endpoint, simplifying the API structure.
Real-time Data: Supports real-time updates with subscriptions.
Strongly Typed Schema: GraphQL APIs are defined by a schema that describes the types of data available and the relationships between them.

When to Use REST:

Simple CRUD applications where the API structure is straightforward.
Scenarios where caching is crucial for performance.
When working with clients that do not require complex querying capabilities.
Legacy systems where REST is already in place.

When to Use GraphQL:

Applications requiring a flexible and efficient data-fetching mechanism.
Complex applications where multiple resources need to be queried simultaneously.
Real-time applications needing subscriptions for live updates.
Projects where minimizing the number of API requests is essential for performance.

Conclusion :

┍ Choosing between GraphQL and REST depends on the specific needs and constraints of your project. REST is a proven and reliable approach, especially for simple, scalable APIs. On the other hand, GraphQL offers a more flexible and efficient way to interact with your data, particularly suited for complex applications and real-time requirements.