MCP vs REST API vs Function Calling : quand utiliser quoi

When building integrations with project management tools, you have three main approaches: Model Context Protocol (MCP), REST APIs, and function calling. This guide helps you choose the right tool for your use case.

Understanding the Three Approaches

MCP

Model Context Protocol - Standardized protocol for AI assistants

Interactive AI assistants
Tool discovery
Natural language
Multi-server support

REST API

Representational State Transfer - Direct HTTP integration

Direct integrations
Custom applications
Full control
Standard HTTP

Function Calling

Model-specific tool integration

Single model
Custom schemas
Tight coupling
Model-specific

When MCP is Ideal

MCP shines in these scenarios:

✅ Best For: Interactive AI Assistants

Natural Language Interaction: Users talk to AI, AI uses tools
Tool Discovery: AI automatically discovers available tools
Multi-Server Support: One AI can use tools from multiple services
Standardized Interface: Same protocol works across different servers
Error Handling: Built-in error recovery and user-friendly messages
Streaming Support: Real-time responses via SSE

Example: Weekly Planning with MCP

User: "What tasks are due this week, and which ones are blocked?"

AI (via MCP):

Discovers Corcava MCP server has list_tasks tool
Calls tool with filters for due dates
Analyzes results and identifies blockers
Presents formatted weekly plan

Result: User gets a natural language response with actionable insights

When REST API is Ideal

REST APIs are better for direct integrations:

✅ Best For: Direct Application Integrations

Custom Applications: Your own software integrating with Corcava
Webhooks & Automation: Event-driven workflows
Mobile Apps: Native mobile applications
Backend Services: Server-to-server communication
Full Control: You handle all request/response logic
Performance: Direct HTTP calls, no protocol overhead

Example: Automated Reporting with REST API

Use Case: Daily automated status report sent via email

Implementation:

Cron job runs daily at 9 AM
Script calls REST API: GET /api/tasks?due_this_week=true
Processes JSON response
Generates HTML report
Sends email via SMTP

Result: Automated, scheduled reporting without human interaction

When Function Calling is Ideal

Function calling is model-specific tool integration:

✅ Best For: Model-Specific Integrations

Single Model: Building for one specific AI model
Custom Schemas: Model-specific function definitions
Tight Coupling: Deep integration with model capabilities
Proprietary Features: Using model-specific features
No Standard Needed: Don't need cross-model compatibility

Example: OpenAI Function Calling

Use Case: Custom ChatGPT plugin for Corcava

Implementation:

Define functions in OpenAI format
Model decides when to call functions
Your backend handles function execution
Results fed back to model

Result: Tightly integrated ChatGPT experience

Project Management Scenario Comparison

Let's see how each approach handles a common project management task: "Create a task from meeting notes"

MCP Approach

User: "Create tasks from these meeting notes: [pastes notes]"

AI analyzes notes and extracts action items
AI calls create_task for each action
AI confirms what was created
User sees natural language summary

Pros: Natural interaction, AI handles complexity, user-friendly

Cons: Requires AI client, less control over exact behavior

REST API Approach

Implementation: Custom script or application

Parse meeting notes (NLP library or manual)
Extract action items programmatically
Call POST /api/tasks for each item
Handle responses and errors
Display results in your UI

Pros: Full control, can customize exactly, works offline

Cons: More code to write, must handle all edge cases

Function Calling Approach

Implementation: Model-specific function definitions

Define create_corcava_task function for the model
Model decides when to call function
Your backend executes the function
Results returned to model
Model formats response for user

Pros: Deep model integration, can use model-specific features

Cons: Tied to one model, not portable, custom implementation

Decision Criteria

Criteria	MCP	REST API	Function Calling
Use Case	AI assistants	Direct integrations	Model-specific
Portability	✅ Works across clients	✅ Standard HTTP	❌ Model-specific
Tool Discovery	✅ Automatic	❌ Manual docs	⚠️ Schema-based
Error Handling	✅ Built-in	⚠️ You implement	⚠️ You implement
Multi-Server	✅ Native support	✅ Multiple endpoints	❌ Single model
Control	⚠️ AI decides	✅ Full control	⚠️ Model decides
Setup Complexity	✅ Simple	⚠️ Moderate	⚠️ Moderate
Best For	Interactive AI	Custom apps	Single model

Hybrid Approaches

You don't have to choose just one. Many teams use combinations:

Example: MCP + REST API Hybrid

Scenario: Team uses MCP for interactive AI workflows, REST API for automation

MCP: Weekly planning, status reports, ad-hoc queries
REST API: Automated webhooks, scheduled reports, mobile app
Result: Best of both worlds - interactive AI + reliable automation

Real-World Recommendations

For Teams Using AI Assistants

Choose: MCP

If your team uses Claude Desktop, Cursor, Windsurf, or Continue, MCP is the natural choice. It provides a standardized way for your AI to interact with Corcava, and you can use the same setup across different AI clients.

Démarrer avec MCP →

For Custom Applications

Choose: REST API

If you're building a custom application, mobile app, or automation script, use the REST API. You have full control over requests and responses, and you're not dependent on AI client availability.

Voir la doc REST API →

For Model-Specific Features

Choose: Function Calling

If you need features specific to one AI model (like OpenAI's structured outputs or Anthropic's tool use), function calling gives you direct access to those capabilities.

Migration Considerations

If you're currently using one approach and considering switching:

REST API → MCP

When: You want to add AI assistant capabilities

Keep REST API for automation
Add MCP for interactive AI
Both can coexist
Same API key can work for both

Function Calling → MCP

When: You want portability across AI clients

MCP works with multiple clients
Standardized protocol
Less model-specific code
Easier to maintain

Summary: Quick Decision Guide

Choose MCP if:

✅ You use AI assistants (Claude, Cursor, etc.)
✅ You want natural language interaction
✅ You need tool discovery
✅ You want to use multiple MCP servers
✅ You prefer standardized protocols

Choose REST API if:

✅ You're building custom applications
✅ You need full control over requests
✅ You want scheduled/automated workflows
✅ You're building mobile apps
✅ You don't need AI interaction

Choose Function Calling if:

✅ You're building for one specific model
✅ You need model-specific features
✅ You want tight integration
✅ Portability isn't important

Ready to Get Started?

Choose the right approach for your use case

Start with MCP →Voir la doc REST API