Taranis AI MCP server

Taranis MCP Server

<!-- mcp-name: io.github.taranis-ai/taranis-mcp-server -->

A read-oriented Model Context Protocol server for Taranis AI. The current version exposes the liststories and listsources tools over stdio or authenticated Streamable HTTP.

Configuration

You need a reachable Taranis instance and a user with ASSESSACCESS. Set TARANISAPI_URL to the complete API root, including /api, then choose exactly one authentication mode:

• TARANISUSERNAME and TARANISPASSWORD: the server logs in lazily and can log in again after an expired JWT.
• TARANISACCESSTOKEN: use an existing JWT returned by Taranis /api/auth/login; it cannot be renewed without credentials.

Do not reuse a Taranis JWT as MCPACCESSTOKEN. The latter protects the MCP HTTP endpoint and is not needed for stdio.

Installation and MCP Client Configuration

This server is intended for desktop AI assistants as well as developer tools. Desktop applications with MCP support include Claude Desktop and OpenAI's ChatGPT desktop app. IDE and terminal clients include Cursor, Zed, Codex CLI, and the Codex IDE extension. If another assistant, such as Mistral Le Chat, offers MCP integration in your installed version or workspace, use its local stdio or remote Streamable HTTP configuration as appropriate.

Clients that support local stdio can launch the server as a child process. The configuration file location and surrounding schema depend on the client, but the server command, arguments, and environment are the same. Desktop applications that support only remote MCP connectors should use the Streamable HTTP setup below instead.

PyPI with uvx

This option installs and runs the package without cloning the repository. Install uv, then add the following definition to a client that uses the mcpServers format, such as Claude Desktop or Cursor:

{
  "mcpServers": {
    "taranis": {
      "command": "/absolute/path/to/uvx",
      "args": ["taranis-mcp-server"],
      "env": {
        "MCP_TRANSPORT": "stdio",
        "TARANIS_API_URL": "https://taranis.example/api",
        "TARANIS_USERNAME": "analyst",
        "TARANIS_PASSWORD": "change-me"
      }
    }
  }
}

Use which uvx on Linux/macOS or where uvx on Windows to find the executable. On Windows the path may end in uvx.exe. An absolute path is recommended because desktop applications may have a smaller PATH than an interactive shell.

Container with Docker

This option requires only Docker. On Windows, install and start Docker Desktop before launching the MCP client. The client passes its configured environment variables to docker, and the -e arguments forward them into the container:

{
  "mcpServers": {
    "taranis": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e",
        "TARANIS_API_URL",
        "-e",
        "TARANIS_USERNAME",
        "-e",
        "TARANIS_PASSWORD",
        "ghcr.io/taranis-ai/taranis-mcp-server:stable"
      ],
      "env": {
        "TARANIS_API_URL": "https://taranis.example/api",
        "TARANIS_USERNAME": "analyst",
        "TARANIS_PASSWORD": "change-me"
      }
    }
  }
}

The stable tag follows the newest stable container release. Replace it with an exact tag such as 0.1.1 for a reproducible installation. Likewise, append a version to the PyPI argument, such as taranis-mcp-server==0.1.1, to pin that installation.

For either installation method, use a JWT by replacing the username and password with TARANISACCESSTOKEN. In the Docker arguments, also replace the two credential -e entries with "-e", "TARANISACCESSTOKEN".

MCP Registry

Shortened. The full README is on GitHub.