Fctr Id_okta Mcp Server

What is Fctr Id_okta Mcp Server?

The Okta MCP Server (v0.2.0-ALPHA) is a tool that enables AI models to interact directly with an Okta environment using the Model Context Protocol (MCP). It is built for IAM engineers, security teams, and Okta administrators to manage and analyze Okta resources through AI assistants.

How to use Fctr Id_okta Mcp Server?

Install prerequisites (Python 3.8+, Okta tenant, MCP-compatible client). Clone the repo, create a virtual environment, install dependencies, and configure a .env file with your Okta domain and API token. Run in STDIO mode (recommended) by configuring your MCP client (e.g., Claude Desktop JSON config) to launch main.py with environment variables. For SSE mode (advanced, with explicit risk acknowledgment) run python main.py --sse --iunderstandtherisks. Docker deployment is also available.

Key features of Fctr Id_okta Mcp Server

• List and get Okta users with filtering and search
• List groups, group members, and group application assignments
• List applications, application users, and group assignments
• Get policy rules and network zones with detailed configuration
• Retrieve system log events with time-based filtering
• Date/time utilities for natural language time expressions

Use cases of Fctr Id_okta Mcp Server

• Find all locked users and create a spreadsheet in Google Drive
• Retrieve the most recently created 20 users for quick review
• List users who haven’t logged in for 90+ days (limited to 50 results)
• Inspect authentication factors enrolled for a specific user
• List all applications assigned to a particular group

FAQ from Fctr Id_okta Mcp Server

What data is sent to the AI model when using this server?

All data returned by the tools (user profiles, group memberships, etc.) is sent to and stored in the LLM’s context for the duration of the conversation. You must be comfortable with Okta data being processed by the LLM provider’s systems.

Are there limits on how many users I can query at once?

Yes. MCP is designed for lightweight workflows. Limit requests to fewer than 100 entities per transaction. Avoid bulk operations like fetching all 10,000 users.

What transport methods are available and which is recommended?

STDIO (Standard I/O) is recommended for direct, secure communication. SSE (Server-Sent Events) over HTTP is available for advanced use only but has significant security risks—no authentication and anyone on the network can issue commands.

What AI providers does this server support?

It supports OpenAI, Azure OpenAI, Anthropic, Google Vertex AI, and any OpenAI-compatible endpoint (e.g., Fireworks.ai, Ollama).

Which AI models are compatible with this server?

Testing has been performed with GPT-4.0, Claude 3.7 Sonnet, and Google-2.5-pro. Only models with explicit tool calling/function calling capabilities will work.