illumio-mcp-server

[](https://mseep.ai/app/alexgoller-illumio-mcp-server) # Illumio MCP Server A Model Context Protocol (MCP) server that provides an interface to interact with Illumio PCE (Policy Compute Engine). This server enables programmatic access to Illumio workload management, label operations, traffic flow analysis, automated ringfencing, and infrastructure service identification. <a href="https://glama.ai/mcp/servers/xhqzxlo9iy"> <img width="380" height="200" src="https://glama.ai/mcp/servers/xhqzxlo9iy/badge" alt="Illumio Server MCP server" /> </a> ## What can it do? Use conversational AI to talk to your PCE: - **Full CRUD** on workloads, labels, IP lists, services, and rulesets - **Traffic analysis** — query flows, get summaries, filter by policy decision - **Automated ringfencing** — analyze traffic and create app-to-app segmentation policies with one command - **Selective enforcement** — add deny rules for apps in selective mode with configurable consumer flavors - **Infrastructure service identification** — discover which apps are infrastructure services using graph centrality analysis, so you know what to policy first - **Deny rule management** — create, update, and delete deny rules (including override deny for emergencies) - **Event monitoring** — query PCE events with severity and type filters - **PCE health checks** — verify connectivity and credentials ## Prerequisites - Python 3.8+ - Access to an Illumio PCE instance - Valid API credentials for the PCE ## Installation 1. Clone the repository: ```bash git clone https://github.com/alexgoller/illumio-mcp-server.git cd illumio-mcp-server ``` 2. Install dependencies: ```bash uv sync ``` ## Configuration You should run this using the `uv` command, which makes it easier to pass in environment variables and run it in the background. ## Using uv and Claude Desktop On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json` On Windows: `%APPDATA%/Claude/claude_desktop_config.json` Add the following to the `custom_settings` section: ```json "mcpServers": { "illumio-mcp": { "command": "uv", "args": [ "--directory", "/path/to/illumio-mcp-server", "run", "illumio-mcp" ], "env": { "PCE_HOST": "your-pce-host", "PCE_PORT": "your-pce-port", "PCE_ORG_ID": "1", "API_KEY": "api_key", "API_SECRET": "api_secret" } } } } ``` ## HTTP transport with OAuth Resource Server (Phase 3a) The server runs over HTTP using the MCP Streamable HTTP transport (spec rev 2025-03-26) and validates OAuth 2.1 bearer tokens issued by your IdP. This is **Phase 3a**: identity is enforced; per-user PCE keys land in Phase 3b. ### Running with auth (production-shaped) ```bash export MCP_PUBLIC_URL=https://mcp.illumio.example export MCP_OAUTH_ISSUER=https://login.microsoftonline.com/<tenant-id>/v2.0 export MCP_OAUTH_JWKS_URL=https://login.microsoftonline.com/<tenant-id>/discovery/v2.0/keys export MCP_OAUTH_AUDIENCE=https://mcp.illumio.example export MCP_OAUTH_REQUIRED_SCOPE=illumio-mcp.use # default; override if needed illumio-mcp-http --host 127.0.0.1 --port 8080 ``` The server refuses to start without these env vars (unless `MCP_DEV_INSECURE=1`). MCP clients discover the AS via the standard RFC 9728 endpoint: ``` GET /.well-known/oauth-protected-resource ``` Unauthenticated requests to `/mcp` return `401` with `WWW-Authenticate: Bearer resource_metadata="<URL>"`, which any spec-compliant MCP client (Claude Desktop, ChatGPT, MCP Inspector) follows automatically to run PKCE auth code flow against the configured AS. ### Running without auth (dev only) ```bash MCP_DEV_INSECURE=1 illumio-mcp-http ``` The server logs a prominent warning. Do NOT use in production. ### Health endpoints (always unauthenticated) - `GET /healthz` — liveness - `GET /readyz` — readiness (Phase 3a returns the same as healthz; Phase 3b/c will add PCE + JWKS reachability) ### Two PCE modes (Phase 3b vs Phase 3e) The HTTP server supports two ways to source PCE credentials, selected via `MCP_PCE_MODE`: | Mode | `MCP_PCE_MODE` | PCE creds | Onboarding | PCE-side audit | |---|---|---|---|---| | **Per-user** (default) | `per_user` | One PCE API key per authenticated user, encrypted in keystore | User registers via `/setup` page or `register-pce-credentials` tool | PCE logs show the real human via per-user API key | | **Shared** | `shared` | One PCE service-account key from env (same as stdio) | None — works immediately for any authenticated user | PCE logs show the service account; the MCP audit log is the source of truth for "who did what" | **Choose per-user when:** - You want PCE-side audit attribution to identify the human - Users are happy to provide their own PCE API key once - You can tolerate the per-user PCE key sprawl (PCE has limits) **Choose shared when:** - The PCE limits API keys per user too aggressively for per-user mode - You want zero-friction onboarding (no `/setup` step) - You're OK relying on the MCP audit log alone for human-level attribution - You operate the PCE service account yourself and rotate it on a schedule In **shared** mode, `/setup` is not mounted, the credential-management tools (`register-pce-credentials`, `delete-pce-credentials`) refuse with a friendly error, and `MCP_KEK` is not required. SSO + JWT + role-based authz + audit log + confirm tokens all still apply identically. ```bash # Shared mode — same env that stdio uses today, plus auth/role config export MCP_PCE_MODE=shared export PCE_HOST=https://your-pce.example.com export PCE_PORT=8443 export PCE_ORG_ID=1 export API_KEY=your_pce_api_key_name export API_SECRET=your_pce_api_key_secret # (other auth/role env vars from earlier sections still apply) illumio-mcp-http ``` ### Per-user PCE keys (Phase 3b) Each authenticated user has their own PCE API key/secret stored in an encrypted SQLite keystore. PCE-side audit logs attribute correctly per human; revoking a user is a single tool call. Additional env required when running with auth: ```bash export MCP_KEK=$(python -c 'import os, base64; print(base64.b64encode(os.urandom(32)).decode())') export MCP_KEYSTORE_PATH=/var/lib/illumio-mcp/keys.db # default: ./data/keys.db ``` The KEK is **never** stored next to the database. Loss of KEK = total loss of stored creds (intentional, fail-closed). For production, source MCP_KEK from KMS or Vault rather than the operator's shell. Onboarding paths (either works): 1. **Browser** — visit `/setup` after authenticating; paste credentials in the form. 2. **MCP client** — call the `register-pce-credentials` tool; the only tool available before credentials are registered. Other credential tools: - `check-pce-credentials-status` — does this user have credentials registered? - `delete-pce-credentials` — remove this user's credentials. ### Role-based authorization (Phase 3c) The server maps each user's IdP groups to one of three internal roles: **reader**, **operator**, **admin**. Per-tool authorization is enforced by the dispatcher using the `roles` metadata on each `ToolSpec`. Configure group → role mapping via env (comma-separated): ```bash # A user matching ANY of these groups gets that role; highest role wins. export MCP_ROLE_GROUPS_ADMIN=sg-illumio-mcp-admin export MCP_ROLE_GROUPS_OPERATOR=sg-illumio-mcp-operator,sg-illumio-mcp-admin export MCP_ROLE_GROUPS_READER=sg-illumio-mcp-readonly,sg-illumio-mcp-operator,sg-illumio-mcp-admin # Optional: fallback role when no group matches. Leave unset to refuse. # export MCP_ROLE_DEFAULT=reader ``` Tool-by-tool defaults: | Tool category | Roles allowed | Examples | |---|---|---| | Reads | reader, operator, admin | `get-labels`, `get-workloads`, `get-traffic-flows` | | Writes | operator, admin | `create-*`, `update-*`, `delete-*` | | Provisioning + bulk | admin | `provision-policy`, `ringfence-batch` | A user without a matching role (and no `MCP_ROLE_DEFAULT`) receives a structured `forbidden_no_role` error. ### Audit log (Phase 3c) Every dispatcher decision (allow / deny / error) is written to a SQLite audit database. Schema and storage location: ```bash # Defaults to <keystore_dir>/audit.db export MCP_AUDIT_LOG_PATH=/var/lib/illumio-mcp/audit.db ``` Audit rows include `(ts, sub, iss, tool, decision, reason, role, request_id)` — **never** tool arguments. The `request_id` matches the `X-Request-Id` response header so external traces can be correlated. Query examples: ```sql -- Recent denied calls per user SELECT ts, sub, tool, reason FROM audit_log WHERE decision='denied' ORDER BY ts DESC LIMIT 20; -- Tool-call volume by user SELECT sub, COUNT(*) FROM audit_log WHERE ts > date('now', '-7 days') GROUP BY sub ORDER BY 2 DESC; ``` ### Confirm tokens for mutating tools (Phase 3d) Tools marked `requires_confirm=True` (currently `provision-policy`, `ringfence-batch`, `register-pce-credentials`, `delete-pce-credentials`) require a server-issued single-use confirm token in `params._meta.confirm_token` when called over HTTP. Stdio mode is unaffected — the operator who launched the process can call mutating tools directly. Required env in auth mode: ```bash export MCP_CONFIRM_HMAC_KEY=$(python -c 'import os, base64; print(base64.b64encode(os.urandom(32)).decode())') # Optional: # export MCP_CONFIRM_TTL_SECONDS=120 # export MCP_CONFIRM_JTI_PATH=/var/lib/illumio-mcp/jti.db # export MCP_CONFIRM_FRESH_AUTH_SECONDS=300 # require JWT auth_time within 5 min ``` #### How a client uses it 1. Call the mutating tool without a token → server returns: ```json {"error": "confirm_required", "params_hash": "<sha256>", "message": "..."} ``` 2. Call `POST /confirm` with the JWT and the params_hash: ```bash curl -X POST https://mcp.illumio.example/confirm \ -H "Authorization: Bearer $JWT" \ -H "Content-Type: application/json" \ -d '{"tool":"provision-policy","params_hash":"<sha256>"}' # → {"confirm_token": "...", "expires_in": 120} ``` 3. Re-call the tool with the token in `params._meta.confirm_token`. Tokens are **single-use** (replays return `confirm_token_replay`) and **scoped** to `(sub, tool, params_hash)`. Tampering with any field invalidates the token. #### Step-up auth (optional, recommended for production) Set `MCP_CONFIRM_FRESH_AUTH_SECONDS=300` to require the JWT's `auth_time` claim to be within the last 5 minutes. Forces the user to re-authenticate before minting a token — the strongest prompt-injection defense available without an interactive session model. Requires the IdP to issue `auth_time` (Entra and Okta both do for OIDC sign-in flows). ## Tools ### Workload Management - `get-workloads` — Retrieve workloads with optional filtering by name, hostname, IP, labels, and max results - `create-workload` — Create an unmanaged workload with name, IP addresses, and labels - `update-workload` — Update an existing workload's properties - `delete-workload` — Remove a workload from PCE ### Label Operations - `get-labels` — Retrieve labels with optional filtering by key, value, and max results - `create-label` — Create a new label with key-value pair - `update-label` — Update an existing label - `delete-label` — Remove a label ### Ruleset & Rule Management - `get-rulesets` — Get rulesets with optional filtering by name, description, and enabled status - `create-ruleset` — Create a new ruleset with scopes - `update-ruleset` — Update ruleset properties - `delete-ruleset` — Remove a ruleset - `create-deny-rule` — Create a deny rule (regular or override deny) in a ruleset - `update-deny-rule` — Update an existing deny rule - `delete-deny-rule` — Remove a deny rule ### IP List Management - `get-iplists` — Get IP lists with optional filtering by name, description, FQDN, and max results - `create-iplist` — Create a new IP list - `update-iplist` — Update an existing IP list - `delete-iplist` — Remove an IP list ### Service Management - `get-services` — Get services with optional filtering by name, port, protocol, and max results - `create-service` — Create a new service definition - `update-service` — Update an existing service - `delete-service` — Remove a service ### Traffic Analysis - `get-traffic-flows` — Get detailed traffic flow data with filtering by date range, source/destination, service, policy decision, and more - `get-traffic-flows-summary` — Get aggregated traffic summaries grouped by app, env, port, and protocol ### Automated Ringfencing - `create-ringfence` — **Automated app-to-app segmentation policy creation.** Analyzes traffic flows to discover which remote apps communicate with a target app, then creates a ruleset with: - **Intra-scope allow rule** — all workloads within the app can communicate freely - **Extra-scope allow rules** — each discovered remote app gets an allow rule on All Services - **Selective enforcement mode** (`selective=true`) — adds a deny rule blocking all inbound, with allow rules for known apps processed first. Gets you to enforcement faster than full enforcement mode. - **Deny consumer flavors** (`deny_consumer` parameter): - `any` (default) — IP list Any (0.0.0.0/0) as consumer, deny only at destination. Safest. - `ams` — All Workloads as consumer, deny pushed to every managed workload. Broader. - `ams_and_any` — Both. Maximum coverage. - **Policy coverage awareness** — each rule is annotated as `already_allowed` (traffic covered by existing policy, created for documentation) or `newly_allowed` (filling a policy gap). Summary shows how many remote apps are already covered vs need new rules. - **`skip_allowed` parameter** — set to `true` to only create rules for traffic not yet covered by existing policy, producing minimal rulesets that fill gaps only - **Merge-safe** — detects existing rulesets and rules, never creates duplicates - **Dry-run support** — preview what would be created without making changes ### Infrastructure Service Identification - `identify-infrastructure-services` — **Discover which apps are infrastructure services** by analyzing traffic patterns. Builds an app-to-app communication graph and uses **dual-pattern scoring** to recognize two types of infrastructure: **Provider infra** (AD, DNS, shared DB) — consumed by many apps, high in-degree, low out-degree. **Consumer infra** (monitoring, backup, log shipping) — connects out to many apps, high out-degree, low in-degree. Two scores are computed per app, and the higher one wins: | Score | Degree metric (40%) | Directionality (30%) | Betweenness (25%) | Volume (5%) | |---|---|---|---|---| | **Provider** | In-degree | Consumer ratio (in/total) | Betweenness centrality | Connection volume | | **Consumer** | Out-degree | Producer ratio (out/total) | Betweenness centrality | Connection volume | **Mixed-traffic dampening:** `score *= 1 / (1 + min(in_degree, out_degree) * 0.3)` — apps with both significant inbound AND outbound connections are business apps, not infrastructure. Pure directional apps (all in OR all out) get no penalty. Non-production environments (staging, dev, etc.) receive a **50% score penalty** since infrastructure services typically live in production. Apps are classified into tiers: - **Core Infrastructure** (score >= 75) — monitoring, AD, SIEM, DNS. Policy these first. - **Shared Service** (score >= 50) — shared databases, message queues. Policy these second. - **Standard Application** (score < 50) — normal business apps. Each result includes a `dominant_pattern` field ("provider" or "consumer") indicating which type of infrastructure the app resembles. **Why this matters:** Infrastructure services are consumed by many apps OR connect out to many apps. If you ringfence apps without allowing infrastructure services first, you break dependencies. This tool tells you what to policy first. ### Policy Lifecycle - `provision-policy` — **Provision pending draft changes** to move them from draft to active state. Can provision all pending changes or specific items by href. Includes change descriptions for audit trail. - `compare-draft-active` — **Compare draft vs active policy** to preview what would change on provisioning. Shows created, updated, and deleted rulesets, rules, IP lists, and services. ### Enforcement Readiness - `enforcement-readiness` — **Assess whether an app is ready for enforcement.** Analyzes traffic flows, existing policy coverage, enforcement modes, and ringfence status. Returns a readiness score (0-100) with actionable recommendations: - **Policy coverage** (40 points) — what percentage of traffic is covered by rules - **Ringfence exists** (20 points) — has a ringfence ruleset been created - **Enforcement mode** (20 points) — are workloads in full/selective/visibility_only - **No blocked traffic** (10 points) — no unintended blocks - **All remote apps covered** (10 points) — no uncovered remote app traffic ### Batch Operations - `ringfence-batch` — **Ringfence multiple apps at once.** Optionally uses `identify-infrastructure-services` to auto-order apps by infrastructure score (infrastructure first, then standard apps). Supports dry-run mode to preview all changes before applying. ### Workload Enforcement Status - `get-workload-enforcement-status` — **Get enforcement mode status across workloads**, grouped by app and environment. Shows counts per mode (idle, visibility_only, selective, full) and identifies apps with **mixed enforcement states** — a common issue during rollouts. ### Policy Coverage - `get-policy-coverage-report` — **Generate a policy coverage report** for an app showing what traffic is covered by existing rules vs what would be blocked. Breaks down by inbound/outbound, identifies uncovered services and remote apps, and provides an overall coverage percentage. - `find-unmanaged-traffic` — **Find traffic involving unmanaged workloads** or IP addresses. These are sources/destinations without app/env labels, representing policy blind spots. Filters by direction (inbound/outbound/both) and connection count. ### Security Analysis - `detect-lateral-movement-paths` — **Detect potential lateral movement paths** by analyzing app-to-app traffic patterns. Identifies articulation points (bridge nodes) whose compromise would provide access to otherwise disconnected app groups. Computes reachability from any starting app and traces multi-hop paths up to a configurable depth. - `compliance-check` — **Check policy compliance** against frameworks (PCI-DSS, NIST 800-53, CIS Controls, or general best practices). Evaluates segmentation, enforcement modes, high-risk port exposure, and policy coverage. Returns a compliance score with per-check findings (PASS/FAIL/WARNING). ### Event Monitoring - `get-events` — Get PCE events with optional filtering by event type, severity, status, and result limits ### Connection Testing - `check-pce-connection` — Verify PCE connectivity and credentials ## Testing The project includes a comprehensive integration test suite that runs against a real PCE using the MCP protocol. ```bash # Set up credentials in .env cat > .env << EOF PCE_HOST=your-pce-host PCE_PORT=8443 PCE_ORG_ID=1 API_KEY=your-api-key API_SECRET=your-api-secret EOF # Run all tests uv run pytest tests/ -v ``` The test suite covers: - Tool listing and schema validation - Full CRUD lifecycle for workloads, labels, IP lists, services, rulesets, and deny rules - Traffic flow queries and summaries - Ringfence creation (standard, selective, deny consumer flavors, merge idempotency) - Infrastructure service identification (scoring, sorting, tier classification) - Error handling for missing resources ## Illumio Rule Processing Order Understanding rule processing is essential for ringfencing: 1. **Essential rules** — built-in, cannot be modified 2. **Override Deny rules** — block traffic overriding all allows (emergency use) 3. **Allow rules** — permit traffic (ringfence remote app rules go here) 4. **Deny rules** — block specific traffic (ringfence deny-all-inbound goes here) 5. **Default action** — selective mode = allow-all, full enforcement = deny-all In selective enforcement, the default is allow-all, so a deny rule is needed to make the ringfence effective. Known remote apps get allow rules (step 3) which are processed before the deny (step 4). ## Visual Examples All the examples below were generated by Claude Desktop and with data obtained through this MCP server. ### Application Analysis  *Detailed view of application communication patterns and dependencies*  *Analysis of traffic patterns between different application tiers* ### Infrastructure Insights  *Overview dashboard showing key infrastructure metrics and status*  *Detailed analysis of infrastructure service communications* ### Security Assessment  *Comprehensive security analysis report*  *Security assessment findings for high-risk vulnerabilities*  *PCI compliance assessment findings*  *SWIFT compliance assessment findings* ### Remediation Planning  *Overview of security remediation planning*  *Detailed steps for security remediation implementation* ### Policy Management  *Management interface for IP lists*  *Overview of ruleset categories and organization*  *Configuration of application ruleset ordering* ### Workload Management  *Detailed workload analysis and metrics*  *Identification and analysis of workload traffic patterns* ### Label Management  *Organization of PCE labels by type and category* ### Service Analysis  *Automatic inference of service roles based on traffic patterns*  *Analysis of top 5 traffic sources and destinations* ### Project Planning  *Project implementation timeline and milestones* ## Available Prompts ### Ringfence Application The `ringfence-application` prompt helps create security policies to isolate and protect applications by controlling inbound and outbound traffic. **Required Arguments:** - `application_name`: Name of the application to ringfence - `application_environment`: Environment of the application to ringfence **Features:** - Creates rules for inter-tier communication within the application - Uses traffic flows to identify required external connections - Implements inbound traffic restrictions based on source applications - Creates outbound traffic rules for necessary external communications - Handles both intra-scope (same app/env) and extra-scope (external) connections - Creates separate rulesets for remote application connections ### Analyze Application Traffic The `analyze-application-traffic` prompt provides detailed analysis of application traffic patterns and connectivity. **Required Arguments:** - `application_name`: Name of the application to analyze - `application_environment`: Environment of the application to analyze **Analysis Features:** - Orders traffic by inbound and outbound flows - Groups by application/environment/role combinations - Identifies relevant label types and patterns - Displays results in a React component format - Shows protocol and port information - Attempts to identify known service patterns (e.g., Nagios on port 5666) - Categorizes traffic into infrastructure and application types - Determines internet exposure - Displays Illumio role, application, and environment labels ### How to use MCP prompts Step1: Click "Attach from MCP" button in the interface  Step 2: Choose from installed MCP servers  Step 3: Fill in required prompt arguments:  Step 4: Click Submit to send the configured prompt ### How prompts work - The MCP server sends the configured prompt to Claude - Claude receives context through the Model Context Protocol - Allows specialized handling of Illumio-specific tasks This workflow enables automated context sharing between Illumio systems and Claude for application traffic analysis and ringfencing tasks. ## Docker The application is available as a Docker container from the GitHub Container Registry. ### Pull the container ```bash docker pull ghcr.io/alexgoller/illumio-mcp-server:latest ``` You can also use a specific version by replacing `latest` with a version number: ```bash docker pull ghcr.io/alexgoller/illumio-mcp-server:1.0.0 ``` ### Run with Claude Desktop To use the container with Claude Desktop, you'll need to: 1. Create an environment file (e.g. `~/.illumio-mcp.env`) with your PCE credentials: ```env PCE_HOST=your-pce-host PCE_PORT=your-pce-port PCE_ORG_ID=1 API_KEY=your-api-key API_SECRET=your-api-secret ``` 2. Add the following configuration to your Claude Desktop config file: On MacOS (`~/Library/Application Support/Claude/claude_desktop_config.json`): ```json { "mcpServers": { "illumio-mcp-docker": { "command": "docker", "args": [ "run", "-i", "--init", "--rm", "-v", "/Users/YOUR_USERNAME/tmp:/var/log/illumio-mcp", "-e", "DOCKER_CONTAINER=true", "-e", "PYTHONWARNINGS=ignore", "--env-file", "/Users/YOUR_USERNAME/.illumio-mcp.env", "illumio-mcp:latest" ] } } } ``` Make sure to: - Replace `YOUR_USERNAME` with your actual username - Create the log directory (e.g. `~/tmp`) - Adjust the paths according to your system ### Run Standalone You can also run the container directly: ```bash docker run -i --init --rm \ -v /path/to/logs:/var/log/illumio-mcp \ -e DOCKER_CONTAINER=true \ -e PYTHONWARNINGS=ignore \ --env-file ~/.illumio-mcp.env \ ghcr.io/alexgoller/illumio-mcp-server:latest ``` ### Docker Compose For development or testing, you can use Docker Compose: ```yaml version: '3' services: illumio-mcp: image: ghcr.io/alexgoller/illumio-mcp-server:latest init: true volumes: - ./logs:/var/log/illumio-mcp environment: - DOCKER_CONTAINER=true - PYTHONWARNINGS=ignore env_file: - ~/.illumio-mcp.env ``` Then run: ```bash docker-compose up ``` ## Contributing 1. Fork the repository 2. Create a feature branch 3. Commit your changes 4. Push to the branch 5. Create a Pull Request ## License This project is licensed under the GPL-3.0 License. See the [LICENSE](LICENSE) file for details. ## Support For support, please [create an issue](https://github.com/alexgoller/illumio-mcp-server/issues).