Internship - Study and Implementation of an MCP Layer (H/F)

Internship - Study and Implementation of an MCP Layer (H/F)

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Internship Goals and Duration

This is a 3-month internship.

The objective of this internship is to study, design, and prototype the integration of a Model Context Protocol (MCP) layer on top of the iWE platform.

The MCP layer aims to expose the platform capabilities (data model, business operations, workflows) in a standardized way for LLM-based agents and external intelligent systems.

The final deliverable is a working Proof of Concept (PoC) demonstrating how iWE can be accessed and manipulated through MCP-compatible clients.

Context and Problem Statement

The iWE platform currently exposes a REST API to interact with dynamically defined data models.

With the emergence of LLM agents and AI-native tooling, a new integration paradigm is gaining traction:instead of calling APIs directly, systems expose discoverable capabilities (tools, resources) via MCP.

This raises several technical challenges:

How to map a dynamic, configuration-driven data model to MCP primitives

How to expose CRUD and business operations as MCP tools

How to ensure security, isolation, and access control in this new interaction model

What API changes or abstractions are required to support MCP efficiently

A parallel internship will focus on the development of an MCP-based agent capable of interacting with the iWE platform. Both interns will work in close collaboration to ensure alignment between the MCP server design and the agent’s capabilities, with the objective of delivering fully compatible and coherent solutions.

What You’ll Do

The internship will be structured around exploration, design, and implementation:

Study the iWE platform architecture

Understand how data models and APIs are generated from configuration

Analyze current REST API capabilities and limitations

2. MCP Study and Design

Study the Model Context Protocol specification and ecosystem

Identify how iWE concepts map to MCP:

Design an MCP exposure strategy for dynamic models

3. API Evolution

Identify required changes to the existing REST API:

normalization of endpoints

improved introspection capabilities

Propose an abstraction layer if needed (adapter or gateway)

4. Security Analysis

Study MCP-specific security concerns:

authentication and identity propagation

authorization at tool/resource level depending on user profile

Propose and prototype a secure access model

5. Prototype Development

Implement a basic MCP server exposing part of the iWE platform

Dynamically generate MCP tools from configuration

Connect the MCP server to an existing iWE instance

6. Validation

Test the MCP integration with an MCP-compatible client

Evaluate usability and limitations

Document findings and architectural recommendations

MCP / AI Integration: tools, resources, protocol lifecycle

Key challenges include:

Exposing a dynamic data model in a structured protocol

Maintaining strict security guarantees in an agent-driven context

Designing a scalable and maintainable integration layer

A working MCP PoC server connected to iWE

A technical study covering:

API evolution strategy

security model

Recommendations for production integration

Desired Skills and Experience

Understanding of REST API design

Interest in LLMs, AI agents, and emerging protocols

Knowledge of software architecture and system design

Familiarity with security concepts (auth, access control)