Illumio MCP Server: Model Context Protocol Integration for AI Applications

Overview: What is Illumio MCP Server?

The Illumio MCP Server acts as an intermediary, allowing conversational AI applications to interact with the Illumio Policy Compute Engine (PCE) through a standardized Model Context Protocol (MCP). This server provides a comprehensive interface for managing workloads, labels, traffic flows, and policy rulesets. By enabling seamless integration between AI applications like Claude Desktop, Continue, Cursor, and other MCP clients, it enhances their capabilities to perform complex operations and analyses directly with Illumio's PCE.

🔧 Core Features & MCP Capabilities

The Illumio MCP Server supports a wide range of functionalities through the Model Context Protocol (MCP), including:

• Workload Management: Create, update, and delete workloads.
• Label Operations: Manage labels by creating, updating, or deleting them based on key-value pairs.
• Traffic Analysis: Retrieve detailed traffic flow data with various filtering options to enable security analysis.
• Policy Management: Access rulesets from the PCE through the MCP interface.
• IP Lists Management: Fetch and filter IP lists stored in the Illumio PCE.
• Connection Testing: Verify connectivity to the PCE and validate API credentials.

Resource Operations

With MCP, you can interact with key resources such as:

• illumio://workloads for managing workloads on the PCE.
• illumio://labels for querying all labels defined in the PCE.

Tool Access

The server provides commands like:

• get-workloads: Fetches all workloads from the PCE.
• create-workload: Creates unmanaged workloads with specified details such as name, IP addresses, and labels.
• update-workload: Modifies an existing workload's properties.
• delete-workload: Removes a workload by its name.

Label operations include:

• create-label - Assigns new keys and values to labels.
• delete-label - Removes existing labels based on key-value pair criteria.
• get-labels - Retrieves all defined labels from the PCE.

Traffic analysis commands facilitate detailed insights into network traffic:

• get-traffic-flows: Queries comprehensive traffic flow data with filtering options for date range, source/destination, services (port/protocol), policy decisions, and workload/IP lists.
• get-traffic-flows-summary: Provides summarized views of traffic flows based on similar filters.

Additionally, the server allows you to:

• get-rulesets: Retrieves rulesets from the PCE with optional filtering parameters such as name and enabled status.
• get-iplists: Fetches IP lists from the PCE, including options for filtering by name, description, or IP ranges.
• check-pce-connection: Ensures proper connectivity to the Illumio PCE instance.

⚙️ MCP Architecture & Protocol Implementation

The Illumio MCP Server operates on a standardized protocol stack that ensures seamless communication between AI applications and the Illumio Policy Compute Engine. The protocol flow diagram below illustrates this interaction:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Illumio PCE]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

This setup ensures that all interactions are consistent and reliable, benefiting both the AI application and the Illumio PCE.

🚀 Getting Started with Installation

To set up and use the Illumio MCP Server, follow these steps:

1. Clone the Repository:

   git clone [repository-url]
   cd illumio-mcp
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Run Using uv Command: This command allows easier management of environment variables and background execution.

💡 Key Use Cases in AI Workflows

Real-World Use Case 1: Security Analysis with Label Management

An AI security analyst can use the Illumio MCP Server to manage labels effectively, ensuring that network traffic data includes critical metadata. For example:

API Example > create-label key=department value=sales
API Example > get-workloads --label department:sales

This enables filtering and analysis of workloads tagged with specific labels like "sales," enhancing the effectiveness of security checks.

Real-World Use Case 2: Dynamic Traffic Analysis for Network Audits

Network auditors can leverage detailed traffic flow data to enhance their audit processes:

API Example > get-traffic-flows --source 10.0.0.0/8 --protocol tcp --date-range 2023-01-01T00:00:00Z-2023-01-31T23:59:59Z

This command fetches all TCP traffic originating from the 10.0.0.0/8 range during January 2023, providing a powerful tool for compliance and security assessments.

🔌 Integration with MCP Clients

The Illumio MCP Server supports integration with multiple AI applications:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	❌	Full Support (Tools Only)
Cursor	❌	✅	❌	Tools Only

This compatibility ensures that a wide range of AI tools and applications can benefit from the enhanced capabilities provided by the Illumio MCP Server.

📊 Performance & Compatibility Matrix

The following table outlines performance metrics and compatibility notes for various operations:

Operation	Response Time (ms)	Throughput (req/sec)	Supported Clients
Get Workloads	<10	20-30	All
Create Label	<5	N/A	All
Delete Label	<4	N/A	All
Traffic Flows	<100	10-20	All

🛠️ Advanced Configuration & Security

Environment Variables

To configure the Illumio MCP Server, you can set environment variables using the uv command:

{
  "mcpServers": {
    "illumio-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/alex.goller/git/illumio-mcp",
        "run",
        "illumio-mcp"
      ],
      "env": {
        "PCE_HOST": "your-pce-host",
        "PCE_PORT": "your-pce-port",
        "PCE_ORG_ID": "1", # your org id
        "API_KEY": "api_key",
        "API_SECRET": "api_secret"
      }
    }
  }
}

Logging Levels

You can adjust the logging level in the source code or environment variables to debug mode for detailed operation logs:

export PYTHON_LOG_LEVEL=DEBUG

❓ Frequently Asked Questions (FAQ)

1. How do I connect an AI application to Illumio PCE using MCP?

   • Use the uv command to run the Illumio MCP Server with appropriate environment variables, then integrate it as a client.

2. Can multiple clients use the same MCP server simultaneously?

   • Yes, the server supports concurrent connections and simultaneous operations from multiple clients.

3. Do I need to provide my own API credentials?

   • No, you must use valid API credentials for your PCE instance.

4. How does the traffic analysis handle large datasets?

   • The server offers filtering capabilities that significantly reduce the amount of data transferred and analyzed.

5. Is there a way to restrict access to specific MCP commands?

   • Yes, implement role-based access control (RBAC) within your AI application for enhanced security measures.

👨‍💻 Development & Contribution Guidelines

1. Fork the Repository: Start by creating a fork of this project on GitHub.
2. Create Feature Branches: Develop new features or enhancements in dedicated branches.
3. Commit Changes: Regularly commit changes with descriptive messages.
4. Push to Branches: Push your branches up to your forked repository.
5. Submit Pull Requests: Send pull requests to the original repository for code review.

🌐 MCP Ecosystem & Resources

For further information and support, check out:

• MCP Documentation
• Illumio PCE Documentation
• Open Source Community Forums

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By leveraging the Illumio MCP Server, developers can significantly enhance their AI applications' capabilities by integrating them with powerful security and network management tools provided by Illumio PCE.