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.

Table of Contents

• 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

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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.

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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.

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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.

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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.

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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:

1. Design API Endpoint (2 hours)

   • Define endpoint: GET /api/customers/{email}
   • Design response schema
   • Document parameters

2. 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

1. Add Authentication (4 hours)

   • Implement API key validation
   • Add rate limiting
   • Set up authorization

2. Add Error Handling (4 hours)

   • Handle database errors
   • Validate inputs
   • Return proper HTTP status codes

3. Deploy (4 hours)

   • Set up server infrastructure
   • Configure environment variables
   • Deploy and test

4. 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:

1. 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;

1. 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

2. 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.

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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

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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.

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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 build, easier to maintain, better agent experience.

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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.