This guide shows how to install, configure, and use the Tetra Data Assistant MCP Server. For an overview of available tools and prompts, see the Tetra Data Assistant MCP Server overview page.

📘

NOTE

The Tetra Data Assistant MCP Server is currently available in beta release. For more information, or to get access, contact your customer account leader.

🚧

IMPORTANT

Code and artifacts generated by AI agents using this MCP server should always be reviewed for accuracy, data integrity, and security before use — as with any AI-generated code.

Prerequisites

To install and run the Tetra Data Assistant MCP Server, you need either of the following Python CLI tool installers:

• Python 3.11 or later. To check your Python version, run python3 --version.
• A Python CLI tool installer — either of the following:
  • pipx — Install by running brew install pipx (macOS) or pip install pipx (all platforms). For other methods, see the pipx installation guide.
  • uv — A fast Python package installer and resolver. Install by running brew install uv (macOS) or pip install uv (all platforms). For other methods, see the uv installation guide.

Install the MCP Server

Install by Using pipx

To install the MCP server by using pipx, run the following command:

pipx install ts-mcp-tetra-data-assistant

This installs the server and makes the mcp-tetra-data-assistant command available globally.

Install by Using uv

To install the MCP server by using uv, run the following command:

uv tool install ts-mcp-tetra-data-assistant

This installs the server and makes the mcp-tetra-data-assistant command available globally.

Configure Your AI Coding Assistant

After you install the MCP server, configure your AI coding assistant to use it. The following sections provide instructions for each supported assistant.

For a full guide on configuring custom MCP servers, see the VS Code documentation.

To see all available command-line options, run:

mcp-tetra-data-assistant --help

Augment

To add the Tetra Data Assistant MCP Server to Augment, do the following:

1. Install the PyPI package as described in the Install the MCP Server section.
2. In VS Code, go to Augment Settings > Tools and click the Import from JSON button under MCP. Then, paste the following:

{
  "mcpServers": {
    "Tetra Data Assistant": {
      "command": "mcp-tetra-data-assistant",
      "args": []
    }
  }
}

3. Click Import. The MCP server starts and is available in Augment.

Alternatively, you can manually add the following to your Augment settings.json file:

{
  "augment.advanced": {
    "mcpServers": [
      {
        "name": "Tetra Data Assistant",
        "command": "mcp-tetra-data-assistant"
      }
    ]
  }
}

Claude Code

To add the Tetra Data Assistant MCP Server to Claude Code, do the following:

1. Install the PyPI package as described in the Install the MCP Server section.
2. Run the following command to register the server globally (available in all projects):

claude mcp add --scope user "mcp-tetra-data-assistant" -- mcp-tetra-data-assistant

3. To register it for a single project only (creates or updates .mcp.json in the current directory), run:

claude mcp add --scope project "mcp-tetra-data-assistant" -- mcp-tetra-data-assistant

For more information, see the Claude Code documentation.

Claude Desktop

To add the Tetra Data Assistant MCP Server to Claude Desktop, do the following:

1. Install the PyPI package as described in the Install the MCP Server section.
2. Find the full path to the installed command by running:

which mcp-tetra-data-assistant

3. In Claude Desktop, go to Settings, then select Developer under Desktop Settings.
4. Click the Edit Config button, which opens the claude_desktop_config.json file in your file explorer.
5. Add the following entry under mcpServers, replacing the placeholder with the full path to the mcp-tetra-data-assistant command from step 2:

{
  "mcpServers": {
    "mcp-tetra-data-assistant": {
      "command": "/full/path/to/mcp-tetra-data-assistant",
      "args": []
    }
  }
}

6. Save the file and restart Claude Desktop. The Tetra Data Assistant tools are available in your conversations.

GitHub Copilot

To add the Tetra Data Assistant MCP Server to GitHub Copilot in VS Code, do the following:

1. Install the PyPI package as described in the Install the MCP Server section.
2. Verify that you have VS Code 1.99 or later and the GitHub Copilot extension installed.
3. Find the full path to the installed command by running:

which mcp-tetra-data-assistant

4. Create or edit .vscode/mcp.json in your workspace root and add the following, replacing the placeholder with the full path from the previous step:

{
  "servers": {
    "mcp-tetra-data-assistant": {
      "type": "stdio",
      "command": "/full/path/to/mcp-tetra-data-assistant",
      "args": []
    }
  }
}

Cursor

To add the Tetra Data Assistant MCP Server to Cursor, do the following:

1. Install the PyPI package as described in the Install the MCP Server section.
2. Find the full path to the installed command by running:

which mcp-tetra-data-assistant

3. In Cursor, go to Settings > Cursor Settings > MCP.
4. Click Add new global MCP server and add the following, replacing the placeholder with the full path from step 2:

{
  "mcpServers": {
    "mcp-tetra-data-assistant": {
      "command": "/full/path/to/mcp-tetra-data-assistant",
      "args": []
    }
  }
}

For more information, see the Cursor MCP documentation.

Available Tools and Prompts

For a complete list of available tools and prompts, see the Tetra Data Assistant MCP Server overview page.

IDS Validator Configuration

The IDSValidator tool uses the ts-ids-validator tool to check compliance with IDS standards. To enable validating IDS compatibility with previous versions from TDP, configure the MCP server with the path to ts-sdk-cfg.json by using the --config flag. If not configured, the validator uses git tags.

For more information, see the ts-ids-validator README.

Limitations

The following are known limitations of the Tetra Data Assistant MCP Server:

• The MCP server requires a Python CLI tool installer (pipx or uv) to install and run.
• AI-generated code and artifacts must be reviewed for accuracy, data integrity, and security before use.

Documentation Feedback

Do you have questions about our documentation or suggestions for how we can improve it? Start a discussion in TetraConnect Hub. For access, see Access the TetraConnect Hub.

📘

NOTE

Feedback isn't part of the official TetraScience product documentation. TetraScience doesn't warrant or make any guarantees about the feedback provided, including its accuracy, relevance, or reliability. All feedback is subject to the terms set forth in the TetraConnect Hub Community Guidelines.