vais-mcp 1.0.0

MCP server to search private data in Vertex AI Search.

MCP Server for Vertex AI Search

MCP server to search private data in Vertex AI Search.

Tools

• search: Search for Vertex AI Search and returns result chunks. Returns a dictionary with a "response" key. The value of "response" is a list of dictionaries, each containing the title of the source document and the extracted content chunk. Example:

{
  "response": [
    {
      "title": "Sample Document Title 1",
      "content": "Extracted text segment from the document."
    },
    {
      "title": "Sample Document Title 2",
      "content": "Another extracted text segment."
    }
  ]
}

Prerequisites

1. Install uv from Astral or the GitHub README
2. Install Python 3.13 using uv python install 3.13
3. Create a Vertex AI Search app
   i. Official Document

Configuration

Add the following to your server configuration:

{
  "mcpServers": {
    "vais-mcp": {
      "command": "uvx",
      "args": ["vais-mcp@latest"],
      "env": {
        "GOOGLE_CLOUD_PROJECT_ID": "<google_cloud_project_id>",
        "VAIS_ENGINE_ID": "<vais_engine_id>"
      }
    }
  }
}

If you want to run with Docker, you will need to obtain a service account key beforehand and mount its path into the Docker container, configured within your mcp.json:

{
  "mcpServers": {
    "vais-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "GOOGLE_CLOUD_PROJECT_ID",
        "-e",
        "VAIS_ENGINE_ID",
        "-e",
        "USE_MOUNTED_SA_KEY",
        "-v",
        "/your/local/path/to/sa-key.json:/app/secrets/sa-key.json:ro",
        "mrmtsntr/vais-mcp:latest"
      ],
      "env": {
        "GOOGLE_CLOUD_PROJECT_ID": "<google_cloud_project_id>",
        "VAIS_ENGINE_ID": "<vais_engine_id>",
        "USE_MOUNTED_SA_KEY": "true"
      }
    }
  }
}

Note: When using Docker as shown above, ensure the local path /your/local/path/to/sa-key.json correctly points to your service account key file.

Note: You can find the Vertex AI Search engine ID in the app url.

https://console.cloud.google.com/gen-app-builder/locations/<location>/engines/<engine_id>/overview/system...

Optional Parameters

You can configure the following optional parameters in the environment or server configuration:

• vais_location: The location of the Vertex AI Search engine. (Default: "global")
• page_size: The number of documents to retrieve as search results. (Default: 5)
• max_extractive_segment_count: The maximum number of extractive chunks to retrieve from each document. (Default: 2)
• log_level: Specifies the logging level. (Default: "WARNING")
• IMPERSONATE_SERVICE_ACCOUNT: The email address of a service account to impersonate for Google Cloud authentication. See the "Google Cloud Authentication" section for details.
• USE_MOUNTED_SA_KEY: Set to true to indicate that a service account key file is mounted at /app/secrets/sa-key.json inside the container and should be used for authentication. (Default: false) If false, Application Default Credentials (ADC) will be used (unless IMPERSONATE_SERVICE_ACCOUNT is set and it also uses a mounted key as its source). If you set this to true, you must mount your local SA key file to /app/secrets/sa-key.json in the Docker container (e.g., using the -v /path/to/your/local-sa-key.json:/app/secrets/sa-key.json flag with docker run).

Example:

  "env": {
    "GOOGLE_CLOUD_PROJECT_ID": "<google_cloud_project_id>",
    "VAIS_ENGINE_ID": "<vais_engine_id>",
    "VAIS_LOCATION": "us-central1",
    "PAGE_SIZE": "20",
    "MAX_EXTRACTIVE_SEGMENT_COUNT": "8",
    "LOG_LEVEL": "DEBUG",
    "IMPERSONATE_SERVICE_ACCOUNT": "[e-mail]",
    "USE_MOUNTED_SA_KEY": "true"
  }

Google Cloud Authentication

This MCP server authenticates to Google Cloud using the following methods, taking into account the IMPERSONATE_SERVICE_ACCOUNT and USE_MOUNTED_SA_KEY environment variables:

• Service Account Impersonation:

  • If the IMPERSONATE_SERVICE_ACCOUNT environment variable is set to the email address of a target service account, the server will attempt to impersonate that service account.
    • If USE_MOUNTED_SA_KEY is true (and a service account key file is mounted to /app/secrets/sa-key.json in the container), the service account key file at /app/secrets/sa-key.json will be used as the source credentials for impersonation.
    • If USE_MOUNTED_SA_KEY is false, Application Default Credentials (ADC) will be used as the source credentials for impersonation.

• Direct Authentication (No Impersonation):

  • If IMPERSONATE_SERVICE_ACCOUNT is not set:
    • If USE_MOUNTED_SA_KEY is true (and a service account key file is mounted to /app/secrets/sa-key.json), the server will directly use the service account key file at /app/secrets/sa-key.json for authentication.
    • If USE_MOUNTED_SA_KEY is false, the server will use ADC for authentication.

ADC automatically find credentials from the environment, such as your local user credentials (set up via gcloud auth application-default login) or a service account attached to the compute resource. For more details, see the official documentation.

When using Docker via mcp.json: If you set USE_MOUNTED_SA_KEY to "true" in the env section of your mcp.json configuration, and correctly mount your local service account key file to /app/secrets/sa-key.json using the -v flag within the args section, the mounted service account key will be used for authentication as described in the flows above.

Note:

• The account used for authentication must have the "Discovery Engine Viewer" role (roles/discoveryengine.viewer). This is required to access Vertex AI Search resources. For more information about roles, see AI Applications roles and permissions.

• If you are running locally, you can set up ADC by running:

  gcloud auth application-default login
  

• For production environments, it is recommended to use a service account with the minimum required permissions.

Development

Building

To prepare this package for distribution:

1. Sync dependencies and update lockfile:

uv sync

Debugging

You can launch the MCP Inspector using following command:

npx @modelcontextprotocol/inspector uvx vais-mcp@latest GOOGLE_CLOUD_PROJECT_ID=<google_cloud_project_id> VAIS_ENGINE_ID=<vais_engine_id>