Zscaler Aiguard Control Plane

An unofficial Model Context Protocol (MCP) server that provides AI agents (Gemini, Claude, Cursor) with programmatic, REST-based access to the Zscaler AI Guard (Zseclipse) ecosystem.

This MCP server allows your AI assistants to autonomously list, create, update, and delete Detection Policies, Match Rules, LLM Applications, and LLM Providers.

Overview

The server wraps the Zscaler AI Guard Public API (https://api.zsapi.net/aiguard/v1). It handles secure OAuth 2.0 client_credentials authentication natively, meaning your AI agent simply calls high-level tools like create_policy or list_llm_apps, and the server manages the Bearer token lifecycle and REST payloads.

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Prerequisites & Authentication

To use this MCP server, you must provide three pieces of information from your Zscaler tenant.

How to obtain your API Credentials:

1. Log in to your Zscaler AI Guard (or ZIdentity) administration portal.
2. Navigate to the API Clients page.
3. Click Add API Client (or generate a new credential).
4. Ensure the client is granted the necessary scopes (all:read, all:write, all:delete).
5. Copy the Client Secret immediately, as it is only shown once at creation.

Environment Variables

The MCP server reads these credentials directly from your environment variables:

• ZSCALER_CLIENT_ID: The OAuth Client ID generated in Step 3.
• ZSCALER_CLIENT_SECRET: The OAuth Client Secret generated in Step 3.
• ZSCALER_VANITY_DOMAIN: Your Zscaler organization's login domain (e.g., if you log in at https://mycompany.zslogin.net, your vanity domain is mycompany).

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Available MCP Tools

This server maps directly to the Zscaler AI Guard API Reference. All tools expect and return standard JSON payloads.

Detection Policies

Manage AI guard detection policies that inspect and act on prompts and responses.

• list_policy_summaries: Returns a lightweight list of all detection policies (IDs, names, versions, timestamps).
• list_policies: Returns all detection policies with full detector configuration.
• get_policy: Fetch a specific policy by its numeric ID.
• get_policy_by_name: Fetch a specific policy by its exact name.
• create_policy: Creates a new detection policy (requires JSON payload).
• update_policy: Replaces a detection policy completely.
• enable_policy_controls: Partially updates the enabled/disabled state of individual policy controls.
• delete_policy: Permanently deletes a detection policy.
• check_policy_references: Checks if a policy is actively referenced by match rules or LLM applications.

Detection Policy Match Rules

Manage match rules that define the conditions under which a detection policy triggers.

• list_match_rules: Returns all match rules for the tenant.
• get_match_rule / get_match_rule_by_name
• create_match_rule
• update_match_rule
• delete_match_rule

LLM Applications

Manage the LLM applications registered with AI Guard.

• list_llm_applications
• get_llm_application / get_llm_application_by_name
• create_llm_application
• update_llm_application
• delete_llm_application
• check_application_references

LLM Providers

Manage the LLM providers configured for the tenant.

• list_llm_providers
• get_llm_provider / get_llm_provider_by_name
• create_llm_provider
• update_llm_provider
• delete_llm_provider
• check_provider_references

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Installation & Usage

1. Gemini CLI (Native Integration)

The fastest way to install this MCP server into your Gemini CLI is to use the native command.

First, clone this repository:

git clone https://github.com/hshen-ai/zscaler-aiguard-control-plane-mcp.git
cd zscaler-aiguard-control-plane-mcp

Then, run the simplified MCP installation command (ensure you replace the placeholder credentials with your actual ZIdentity keys):

gemini mcp add zscaler-aiguard-control-plane-mcp --env ZSCALER_CLIENT_ID=your_client_id --env ZSCALER_CLIENT_SECRET=your_client_secret --env ZSCALER_VANITY_DOMAIN=your_vanity_domain -- uv run --manifest-path ./pyproject.toml fastmcp run ./server.py

2. Claude Desktop / Claude Code

Add the server to your claude_desktop_config.json or .claude/mcp-servers.json:

{ "mcpServers": { "zscaler-aiguard-control-plane-mcp": { "command": "uv", "args": ["run", "fastmcp", "run", "/path/to/Zscaler-AI-Guard-Control-Plane-MCP/server.py"], "env": { "ZSCALER_CLIENT_ID": "...", "ZSCALER_CLIENT_SECRET": "...", "ZSCALER_VANITY_DOMAIN": "..." } } } }

3. Cursor IDE

In Cursor Settings > Features > MCP, add a new server:

• Name: zscaler-aiguard-control-plane-mcp
• Type: command
• Command: uv run fastmcp run /path/to/Zscaler-AI-Guard-Control-Plane-MCP/server.py