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MCP Server

This example demonstrates how to create and run a SCIM server that exposes its functionality as MCP (Model Context Protocol) tools for AI agents. The MCP integration transforms SCIM operations into a structured tool interface that AI systems can discover, understand, and execute.

What This Example Demonstrates

  • AI-Native SCIM Interface - Complete SCIM operations exposed as discoverable AI tools
  • Tool Schema Generation - Automatic JSON Schema creation for AI agent understanding
  • Multi-Tenant AI Support - Tenant-aware operations for enterprise AI deployment
  • Error-Resilient AI Workflows - Structured error responses enabling AI decision making
  • Schema Introspection - Dynamic discovery of SCIM capabilities and resource types
  • Version-Aware AI Operations - Built-in concurrency control for AI-driven updates

Key Features Showcased

AI Tool Discovery

Watch how ScimMcpServer automatically exposes SCIM operations as structured tools that AI agents can discover and understand without manual configuration.

Structured Tool Execution

See how complex SCIM operations are transformed into simple, parameterized tools that AI agents can execute with natural language input, complete with validation and error handling.

Schema-Driven AI Understanding

The example demonstrates how AI agents can introspect SCIM schemas to understand resource structures, attribute types, and validation rules - enabling intelligent data manipulation.

Enterprise AI Integration

Explore how McpServerInfo provides comprehensive server capabilities to AI agents, enabling sophisticated identity management workflows.

Concepts Explored

This example bridges AI and identity management through several key concepts:

Perfect For Building

This example is essential if you're:

  • Building AI-Powered Identity Systems - Automated user provisioning and management
  • Creating Conversational HR Tools - Natural language identity operations
  • Implementing Smart Workflows - AI-driven identity lifecycle management
  • Enterprise AI Integration - Connecting AI agents to identity infrastructure

AI Agent Capabilities

The MCP server exposes comprehensive identity management tools:

User Management Tools

  • Create User - Provision new user accounts with validation
  • Get User - Retrieve user information by ID or username
  • Update User - Modify user attributes with conflict detection
  • Delete User - Deactivate or remove user accounts
  • List Users - Query and filter user populations
  • Search Users - Find users by specific attributes

Group Management Tools

  • Create Group - Establish new groups with member management
  • Manage Members - Add and remove group members
  • Group Queries - Search and filter group collections

Schema Discovery Tools

  • List Schemas - Discover available resource types and attributes
  • Get Server Info - Understand server capabilities and configuration
  • Introspect Resources - Examine resource structure and validation rules

AI Workflow Examples

The example demonstrates several AI agent interaction patterns:

Conversational User Creation

AI agents can create users from natural language descriptions, automatically mapping human-readable requests to proper SCIM resource structures.

Intelligent Error Recovery

When operations fail, the structured error responses help AI agents understand what went wrong and how to correct the issue.

Multi-Step Workflows

Complex identity operations can be broken down into multiple tool calls, with the AI agent orchestrating the sequence based on business logic.

Schema-Aware Operations

AI agents can inspect schemas before operations, ensuring they provide appropriate data types and required fields.

Running the Example

cargo run --example mcp_server_example --features mcp

The server starts listening on standard input/output for MCP protocol messages, ready to receive tool discovery and execution requests from AI agents.

Integration with AI Systems

This example works with various AI agent frameworks:

  • Claude Desktop - Direct MCP protocol integration
  • Custom AI Agents - JSON-RPC 2.0 protocol support
  • Workflow Automation - Programmatic AI agent integration
  • Enterprise AI Platforms - Structured tool interface compatibility

Production Considerations

The example illustrates enterprise-ready AI integration patterns:

  • Security Boundaries - Tenant isolation for AI operations
  • Audit Trails - Comprehensive logging of AI-driven changes
  • Rate Limiting - Controlled AI agent access patterns
  • Error Handling - Graceful failure modes for AI workflows

Multi-Tenant AI Operations

See how AI agents can work with tenant-scoped operations, enabling:

  • Customer-Specific AI - Agents that operate within tenant boundaries
  • Isolated AI Workflows - Preventing cross-tenant data access
  • Tenant-Aware Automation - Context-sensitive AI operations

Next Steps

After exploring MCP integration:

Source Code

View the complete implementation: examples/mcp_server_example.rs