MCP with ETag Support

This example demonstrates how to combine MCP (Model Context Protocol) integration with ETag-based concurrency control, enabling AI agents to perform version-aware identity management operations. It shows how AI systems can handle concurrent access scenarios and prevent data conflicts through proper version management.

What This Example Demonstrates

Version-Aware AI Operations - AI agents that understand and work with resource versions
Conflict-Resilient AI Workflows - Automatic handling of version conflicts in AI-driven updates
Optimistic Concurrency for AI - Non-blocking AI operations with conflict detection
Intelligent Retry Logic - AI agents that can recover from version conflicts gracefully
Production-Safe AI Integration - Preventing AI-induced data corruption in concurrent environments
Multi-Client AI Scenarios - Multiple AI agents working safely with shared resources

Key Features Showcased

AI-Aware Version Management

See how ScimMcpServer exposes version information to AI agents, enabling them to make informed decisions about when and how to update resources.

Conditional AI Operations

Watch AI agents use version parameters in their tool calls, leveraging the same ConditionalOperations that power HTTP-based concurrency control.

Structured Conflict Responses

Explore how version conflicts are communicated to AI agents through ScimToolResult, providing enough context for intelligent conflict resolution.

AI Retry Patterns

The example demonstrates how AI agents can implement exponential backoff and intelligent retry logic when encountering version conflicts, preventing infinite retry loops.

Concepts Explored

This example combines advanced AI and concurrency concepts:

MCP Integration - AI agent support with advanced features
Concurrency Control - Version-based conflict prevention
Operation Handlers - Framework-agnostic version handling
SCIM Server - Server-level concurrency orchestration

Perfect For Building

This example is essential if you're:

Building Production AI Systems - AI agents that work safely in concurrent environments
Implementing Automated Workflows - AI-driven processes that must handle conflicts gracefully
Creating Resilient AI Agents - Systems that can recover from operational conflicts
Enterprise AI Integration - AI agents working with shared enterprise resources

AI Agent Scenarios

The example covers sophisticated AI interaction patterns:

Collaborative AI Agents

Multiple AI agents working on the same resources simultaneously, with automatic conflict detection and resolution when their operations overlap.

Long-Running AI Workflows

AI agents that perform multi-step operations over time, using version checking to ensure their assumptions about resource state remain valid.

AI-Human Collaboration

Scenarios where AI agents and human administrators work on the same resources, with version control preventing conflicts between automated and manual changes.

Batch AI Operations

AI agents performing bulk updates with version validation, ensuring consistency across multiple related resource changes.

Version-Aware Tool Operations

The MCP server exposes enhanced tools with version support:

User Management with Versions

Get User with Version - Retrieve users with current version information
Update User with Version Check - Conditional updates that respect current versions
Create User with Conflict Detection - Prevent duplicate creation with version validation

Group Operations with Concurrency Control

Modify Group Membership - Version-aware member addition and removal
Update Group Properties - Conditional group updates with conflict detection
Bulk Group Operations - Multiple group changes with consistent versioning

Schema Operations

Version-Aware Schema Discovery - Understanding current schema versions
Schema Extension Validation - Ensuring extensions don't conflict with current state

Running the Example

cargo run --example mcp_etag_example --features mcp

The server demonstrates version-aware AI operations with simulated concurrent access scenarios, showing how AI agents handle conflicts and maintain data consistency.

Data Integrity - Preventing AI-induced data corruption through version validation
System Stability - Avoiding cascade failures from AI retry storms
Operational Reliability - Predictable AI behavior in concurrent scenarios
Audit Compliance - Version tracking for all AI-initiated changes

Multi-Tenant Version Management

See how version control works in multi-tenant AI scenarios:

Tenant-Scoped Versions - Version management within tenant boundaries
Cross-Tenant Conflict Prevention - Ensuring AI agents respect tenant isolation
Per-Tenant AI Policies - Different conflict resolution strategies for different customers

Integration with AI Frameworks

The example works with various AI agent systems:

Autonomous Agents - Self-directing AI systems with conflict awareness
Workflow Orchestrators - Multi-step AI processes with version checkpoints
Decision Support Systems - AI assistants that help humans navigate conflicts
Automated Operations - AI-driven identity lifecycle management

Advanced Features

Explore sophisticated version-aware AI capabilities:

Predictive Conflict Avoidance - AI agents that anticipate and avoid conflicts
Collaborative Decision Making - Multiple AI agents negotiating resource changes
Version History Analysis - AI systems that learn from past conflict patterns
Adaptive Retry Strategies - AI agents that adjust behavior based on conflict frequency

Next Steps

After exploring MCP with ETag support:

ETag Concurrency Control - Understanding the underlying concurrency mechanisms
MCP Server - Full-featured MCP integration without version focus
Multi-Tenant Server - Tenant-aware version management

Source Code

View the complete implementation: examples/mcp_etag_example.rs

Concurrency Control Concepts - Complete concurrency control overview
MCP Integration Guide - AI agent architecture and patterns
MCP API Reference - Complete MCP integration documentation

SCIM Server Guide