MCP Tools vs Custom APIs: What's Better for Agents?

Most teams building AI agents face the same question: should we build MCP tools or custom APIs? Both give agents access to data, but they work differently. The choice matters—it affects how fast you can build, how secure your agents are, and how well they perform.

I've seen teams choose APIs because they're familiar, then realize they need the flexibility MCP tools provide. I've also seen teams build MCP tools when a simple API would have been faster. The right choice depends on your use case.

This guide compares MCP tools and custom APIs head-to-head. You'll learn when to use each, what trade-offs to consider, and how to make the right decision for your agents.

What Are MCP Tools?
What Are Custom APIs?
Key Differences: MCP Tools vs Custom APIs
When to Use MCP Tools
When to Use Custom APIs
Real-World Comparison: Building the Same Feature
Hybrid Approach: Using Both
Common Mistakes to Avoid
Where Pylar Fits In
Frequently Asked Questions

What Are MCP Tools?

MCP (Model Context Protocol) tools are functions designed specifically for AI agents. They're part of a protocol that standardizes how agents interact with external data and systems.

Architecture:

Agent → MCP Tool → Data Source

Characteristics of MCP Tools

1. Agent-Native Design

Built for how agents work, not applications
Natural language descriptions help agents understand what tools do
Structured schemas that agents can reason about

2. Framework-Agnostic

Work with any MCP-compatible framework
Claude Desktop, LangChain, OpenAI, n8n, Zapier, Make—all use the same tools
One tool definition works everywhere

3. Self-Describing

Tools describe themselves in plain English
Agents can discover and understand tools without documentation
Parameters are clearly defined with types and descriptions

Example MCP Tool:

{
  "name": "get_customer_info",
  "description": "Get customer information including name, email, plan, and subscription status",
  "inputSchema": {
    "type": "object",
    "properties": {
      "email": {
        "type": "string",
        "description": "Customer email address"
      }
    },
    "required": ["email"]
  }
}

When an agent needs customer data, it calls this tool. The agent understands what the tool does from its description and knows what parameters to provide.

What Are Custom APIs?

Custom APIs are REST endpoints you build specifically for your agents. They're traditional HTTP APIs designed to serve agent requests.

Architecture:

Agent → HTTP Client → REST API → Data Source

Characteristics of Custom APIs

1. Application-Focused Design

Built for HTTP clients, not agents specifically
Follow REST conventions (GET, POST, PUT, DELETE)
Standard HTTP status codes and error handling

2. Fixed Endpoints

Each endpoint serves a specific purpose
New questions often require new endpoints
Endpoints are documented but not self-describing

3. Language-Agnostic

Any language can call REST APIs
Standard HTTP protocol
Works with any HTTP client

Example Custom API:

# FastAPI endpoint
@app.get("/api/customers/{email}")
async def get_customer(email: str):
    query = "SELECT * FROM customer_support_view WHERE email = :email"
    result = db.execute(query, {"email": email})
    return result

The agent calls this endpoint via HTTP:

const response = await fetch(`https://api.example.com/customers/${email}`, {
  headers: { 'Authorization': `Bearer ${apiKey}` }
});
const customer = await response.json();

Key Differences: MCP Tools vs Custom APIs

Here's how they compare across critical dimensions:

1. Agent Understanding

MCP Tools:

Agents understand tools from their descriptions
Natural language descriptions help agents choose the right tool
Self-describing schemas make it clear what inputs/outputs are expected

Custom APIs:

Agents need documentation to understand endpoints
No built-in way for agents to discover what endpoints do
Agents must be explicitly told which endpoint to use

Winner: MCP Tools. They're designed for agent understanding.

2. Flexibility

MCP Tools:

One tool can answer multiple questions
Parameters allow flexible querying
Tools can be composed and chained

Custom APIs:

Each endpoint serves a specific purpose
New questions often require new endpoints
Less flexible, more rigid structure

Winner: MCP Tools. More flexible for varied agent queries.

3. Framework Support

MCP Tools:

Work with any MCP-compatible framework
One tool definition works everywhere
Framework-agnostic by design

Custom APIs:

Work with any HTTP client
But each framework needs custom integration code
More integration work required

Winner: MCP Tools. Better framework support.

4. Development Speed

MCP Tools:

Can be built quickly with tools like Pylar (under 2 minutes)
Or manually (1-2 weeks for full implementation)
Natural language descriptions make them fast to define

Custom APIs:

Require building endpoints, authentication, error handling
Typically 1-3 days per endpoint
More boilerplate code

Winner: MCP Tools (with tools like Pylar). Faster to build.

5. Security and Governance

MCP Tools:

Query through sandboxed views (when built with Pylar)
Built-in governance through view layer
Access control at the tool level

Custom APIs:

Security depends on your implementation
You build authentication, authorization, validation
More control but more work

Winner: Tie. Both can be secure, but MCP tools with Pylar have governance built-in.

6. Maintenance

MCP Tools:

Self-describing, easier to maintain
Changes propagate automatically (with Pylar)
Less code to maintain

Custom APIs:

More endpoints = more code to maintain
Changes require redeployment
More infrastructure to manage

Winner: MCP Tools. Less maintenance burden.

7. Performance

MCP Tools:

Direct database queries (when using Pylar)
Optimized through sandboxed views
Low latency

Custom APIs:

HTTP overhead (serialization, network)
Additional layer adds latency
But can use caching, CDN

Winner: MCP Tools. Lower latency, direct queries.

8. Cost

MCP Tools:

Platform cost (if using Pylar)
Or engineering time (if building manually)
Lower infrastructure cost

Custom APIs:

Engineering time to build and maintain
Infrastructure cost (servers, load balancers)
Higher total cost

Winner: MCP Tools. Lower total cost.

When to Use MCP Tools

Use MCP tools when:

✅ You Need Agent-Native Interfaces

MCP tools are designed for agents. If you want agents to understand and use your data access layer naturally, MCP tools are the right choice.

Example: A support agent that needs to answer customer questions. The agent should understand what tools are available and choose the right one based on the question.

✅ You're Using Multiple Agent Frameworks

If you're using Claude Desktop, LangChain, OpenAI, n8n, and other frameworks, MCP tools work with all of them. One tool definition, multiple frameworks.

Example: A data team that wants to give multiple frameworks access to customer data. With MCP tools, they build once and use everywhere.

✅ You Need Flexible Querying

MCP tools can answer different questions with the same tool through parameters. You don't need a new tool for every question.

Example: A tool that gets customer info can answer "What's the status of customer@example.com?" and "What's the plan for customer@example.com?" with the same tool.

✅ You Want Fast Development

With tools like Pylar, you can build MCP tools in under 2 minutes. No API development, no deployment pipelines, no infrastructure management.

Example: A data analyst who needs to give an agent access to customer data. They create a view in Pylar, describe the tool in natural language, and it's live in 2 minutes.

✅ You Need Built-in Governance

MCP tools built with Pylar query through sandboxed views. Governance is built-in—you don't need to build security from scratch.

Example: A team that needs SOC2 compliance. Pylar's view layer enforces access boundaries automatically.

When to Use Custom APIs

Use custom APIs when:

✅ You Need HTTP-Based Access

If you need standard HTTP access (for web apps, mobile apps, or other non-agent clients), REST APIs are the right choice.

Example: A web application that needs to display customer data. It calls a REST API endpoint.

✅ You Have Existing API Infrastructure

If you already have REST APIs, it might be faster to add agent-specific endpoints than to build MCP tools.

Example: A company with existing REST APIs for their application. They add new endpoints for agents to use.

✅ You Need Complex Business Logic

If your data access requires complex business logic that's hard to express in SQL, custom APIs give you more flexibility.

Example: An endpoint that calculates customer lifetime value using complex algorithms. This is easier to implement in application code than SQL.

✅ You Need Standard HTTP Features

If you need caching, CDN, load balancing, or other standard HTTP infrastructure, REST APIs are the right choice.

Example: An API that serves high-traffic requests. You want to use a CDN to cache responses globally.

✅ You're Building for Applications, Not Just Agents

If you're building for both applications and agents, REST APIs serve both use cases.

Example: A company that needs to serve both their web application and AI agents. REST APIs work for both.

Real-World Comparison: Building the Same Feature

Let me show you how to build the same feature with both approaches:

The Goal

Build a way for agents to get customer information, including:

Customer name, email, plan
Subscription status
Recent usage (last 30 days)
Open support tickets

Approach 1: Custom API

Time: 2-3 days

Steps:

Design API Endpoint (2 hours)
- Define endpoint: GET /api/customers/{email}
- Design response schema
- Document parameters

Build Endpoint (1 day)

@app.get("/api/customers/{email}")
async def get_customer(email: str):
    # Validate input
    if not email or "@" not in email:
        raise HTTPException(400, "Invalid email")
    
    # Query database
    query = "SELECT * FROM customer_support_view WHERE email = :email"
    result = db.execute(query, {"email": email})
    
    if not result:
        raise HTTPException(404, "Customer not found")
    
    return result

Add Authentication (4 hours)
- Implement API key validation
- Add rate limiting
- Set up authorization
Add Error Handling (4 hours)
- Handle database errors
- Validate inputs
- Return proper HTTP status codes
Deploy (4 hours)
- Set up server infrastructure
- Configure environment variables
- Deploy and test
Document (2 hours)
- Write API documentation
- Create examples
- Update agent code to use API

Total: 2-3 days Code written: ~200 lines Infrastructure: Server, database connections, auth system

Approach 2: MCP Tool (with Pylar)

Time: Under 2 minutes

Steps:

Create View (if you don't have one already) (30 seconds)

CREATE VIEW customer_support_view AS
SELECT 
  customer_id,
  customer_name,
  email,
  plan_name,
  subscription_status,
  active_users_30d,
  open_tickets
FROM customers
WHERE is_active = true;

Create MCP Tool (1 minute)
- Open Pylar's MCP tool builder
- Describe: "Create a tool that gets customer information. It should take a customer email and return customer details, subscription status, recent usage, and open tickets."
- Pylar generates the tool automatically
Publish (30 seconds)
- Click "Publish"
- Get MCP server URL and token
- Done

Total: Under 2 minutes Code written: 0 lines (just SQL view) Infrastructure: None (Pylar handles it)

Comparison

Factor	Custom API	MCP Tool (Pylar)
Time to Build	2-3 days	Under 2 minutes
Code Written	~200 lines	0 lines (just SQL)
Infrastructure	Server, auth, deployment	None (Pylar handles)
Maintenance	Ongoing (20-30% time)	None (Pylar handles)
Framework Support	Custom integration per framework	All MCP frameworks
Agent Understanding	Requires documentation	Self-describing
Flexibility	Fixed endpoint	Flexible parameters

Winner: MCP Tools. 99% faster, zero maintenance, better agent experience.

Hybrid Approach: Using Both

You don't have to choose one or the other. Many teams use both:

When to Use Both

Use MCP Tools For:

Agent-specific data access
Flexible querying needs
Multi-framework support
Fast development

Use Custom APIs For:

Application access (web, mobile)
Complex business logic
Standard HTTP features (caching, CDN)
Existing API infrastructure

Example Hybrid Architecture

Applications → Custom REST APIs → Database
Agents → MCP Tools → Same Database (via views)

Both access the same data, but through different interfaces optimized for their use case.

Benefits:

Applications get standard REST APIs
Agents get agent-native MCP tools
Both query through the same sandboxed views (governance)
One data source, multiple interfaces

Common Mistakes to Avoid

Here are mistakes I've seen teams make:

Mistake 1: Choosing APIs Because They're Familiar

What happens: Teams choose REST APIs because they know how to build them, even when MCP tools would be better.

Why it's a problem: Slower development, worse agent experience, more maintenance.

The fix: Evaluate both options. If you're building for agents, MCP tools are usually better.

Mistake 2: Building MCP Tools When You Need HTTP Access

What happens: Teams build MCP tools when they actually need REST APIs for web/mobile apps.

Why it's a problem: MCP tools aren't designed for HTTP clients. You'll end up building wrappers.

The fix: Use REST APIs for HTTP clients, MCP tools for agents.

Mistake 3: Not Using Governance for Either

What happens: Teams build APIs or MCP tools that query raw database tables directly.

Why it's a problem: No security boundaries, compliance issues, performance problems.

The fix: Always use sandboxed views. Both APIs and MCP tools should query through views, not raw tables.

Mistake 4: Building Separate Systems

What happens: Teams build completely separate systems for APIs and MCP tools.

Why it's a problem: Duplicate code, inconsistent governance, higher maintenance.

The fix: Use the same data views for both. APIs and MCP tools can query the same views.

Mistake 5: Not Considering Framework Support

What happens: Teams build custom APIs, then realize they need to support multiple agent frameworks.

Why it's a problem: Each framework needs custom integration code. More work, more maintenance.

The fix: If you're supporting multiple frameworks, MCP tools are the better choice.

Where Pylar Fits In

Pylar makes MCP tools the obvious choice for most teams. Here's why:

Fast Development: Build MCP tools in under 2 minutes. No API development, no deployment pipelines, no infrastructure management. Just describe what you want, and Pylar creates the tool.

Built-in Governance: Pylar tools query through sandboxed views you create in Pylar's SQL IDE. Views enforce access boundaries, filter sensitive data, and ensure compliance. Both MCP tools and custom APIs benefit from this governance layer.

Framework-Agnostic: Pylar tools work with any MCP-compatible framework. Build once, use everywhere—Claude Desktop, LangGraph, OpenAI, n8n, Zapier, Make, and more. No custom integration code needed.

Self-Service: Data teams can build MCP tools without engineering bottlenecks. No API development, no deployment pipelines, no infrastructure management. Just SQL views and natural language descriptions.

Monitoring Included: Pylar Evals gives you visibility into how agents use your tools. Track success rates, errors, query patterns, and costs. Get alerts when something looks wrong.

Try Pylar free: Sign up at pylar.ai to build your first MCP tool in under 2 minutes. No credit card required.

If you're building for agents, MCP tools with Pylar are almost always the better choice. Faster to bui

Frequently Asked Questions

Can I use both MCP tools and custom APIs?

Yes. Many teams use both:

Custom APIs for applications (web, mobile)
MCP tools for agents

Both can query the same sandboxed views, so you get consistent governance.

Do MCP tools work with REST APIs?

MCP tools are a different protocol than REST. But you can build MCP tools that call REST APIs internally, or build REST APIs that expose MCP tool functionality.

Which is more secure: MCP tools or custom APIs?

Both can be secure. The key is using sandboxed views for governance. Pylar provides this for MCP tools. For custom APIs, you need to build governance yourself.

Can I convert my existing REST APIs to MCP tools?

Yes. You can:

Create sandboxed views that your APIs query
Build MCP tools that query the same views
Gradually migrate agents from APIs to MCP tools

Or use Pylar to build MCP tools quickly, then deprecate APIs over time.

Do MCP tools have better performance than REST APIs?

Yes. MCP tools (when using Pylar) query databases directly through views. REST APIs add HTTP overhead (serialization, network). MCP tools have lower latency.

Which costs more: MCP tools or custom APIs?

MCP tools with Pylar cost less:

Platform cost (free tier available)
No infrastructure to manage
No engineering time for maintenance

Custom APIs cost more:

Engineering time to build (2-3 days per endpoint)
Infrastructure cost (servers, load balancers)
Ongoing maintenance (20-30% engineering time)

Can I use MCP tools with my existing API infrastructure?

Yes. MCP tools can query the same data sources as your APIs. You can use both simultaneously, or gradually migrate from APIs to MCP tools.

What if I need complex business logic?

For complex business logic that's hard to express in SQL:

Use custom APIs (easier to implement in application code)
Or use MCP tools with custom handlers (more complex)

For most data access use cases, SQL views are sufficient, making MCP tools the better choice.

Do I need to know the MCP specification to use Pylar?

No. Pylar handles all MCP protocol details for you. You just describe what you want in natural language, and Pylar creates the tool with proper MCP formatting.

Can I build MCP tools without Pylar?

Yes. You can build MCP tools manually (1-2 weeks) or use template tools (2-4 hours). But Pylar is the fastest option (under 2 minutes) with built-in governance.

For most teams building AI agents, MCP tools are the better choice. They're faster to build, easier to maintain, and provide a better agent experience. With Pylar, you can build MCP tools in under 2 minutes with built-in governance. Try it free at pylar.ai.

If you're building for both applications and agents, use both: REST APIs for applications, MCP tools for agents. Both can query the same sandboxed views for consistent governance.

MCP Tools vs Custom APIs: What's Better for Agents?

Table of Contents

What Are MCP Tools?

Characteristics of MCP Tools

What Are Custom APIs?

Characteristics of Custom APIs

Key Differences: MCP Tools vs Custom APIs

1. Agent Understanding

2. Flexibility

3. Framework Support

4. Development Speed

5. Security and Governance

6. Maintenance

7. Performance

8. Cost

When to Use MCP Tools

✅ You Need Agent-Native Interfaces

✅ You're Using Multiple Agent Frameworks

✅ You Need Flexible Querying

✅ You Want Fast Development

✅ You Need Built-in Governance

When to Use Custom APIs

✅ You Need HTTP-Based Access

✅ You Have Existing API Infrastructure

✅ You Need Complex Business Logic

✅ You Need Standard HTTP Features

✅ You're Building for Applications, Not Just Agents

Real-World Comparison: Building the Same Feature

The Goal

Approach 1: Custom API

Approach 2: MCP Tool (with Pylar)

Comparison

Hybrid Approach: Using Both

When to Use Both

Example Hybrid Architecture

Common Mistakes to Avoid

Mistake 1: Choosing APIs Because They're Familiar

Mistake 2: Building MCP Tools When You Need HTTP Access

Mistake 3: Not Using Governance for Either

Mistake 4: Building Separate Systems

Mistake 5: Not Considering Framework Support

Where Pylar Fits In

Frequently Asked Questions

Can I use both MCP tools and custom APIs?

Do MCP tools work with REST APIs?

Which is more secure: MCP tools or custom APIs?

Can I convert my existing REST APIs to MCP tools?

Do MCP tools have better performance than REST APIs?

Which costs more: MCP tools or custom APIs?

Can I use MCP tools with my existing API infrastructure?

What if I need complex business logic?

Do I need to know the MCP specification to use Pylar?

Can I build MCP tools without Pylar?