Which integrations are available for this server?

Provides access to Google Cloud Datastore for entity operations (create, read, update, delete), querying with filters and ordering, namespace support, and batch operations.

How do I use Datastore MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Datastore MCP Server list all entities of kind 'Task'" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Datastore MCP Server

by hashkrish

Overview Schema Related Servers Score Discussions

Python

Remote

Datastore MCP Server

A Model Context Protocol (MCP) server that provides access to Google Cloud Datastore. This server enables AI assistants like Claude to interact with Datastore entities, perform queries, and manage data.

Features

Entity Operations: Create, read, update, and delete Datastore entities
Query Support: Execute queries with filters, ordering, and pagination
Namespace Support: Work with different Datastore namespaces
Emulator Support: Connect to local Datastore emulator by default for development
Production Ready: Easy configuration for production Google Cloud Datastore

Related MCP server: Firestore MCP Server

Installation

Prerequisites

Option 1: Docker (Recommended)

Docker Engine 20.10+
Docker Compose v2.0+

Option 2: Local Python

Python 3.10 or higher
Google Cloud Datastore emulator (for local development) or Google Cloud project (for production)

Install from source

# Clone the repository
git clone <repository-url>
cd datastore-mcp

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -e .

Using uv (recommended)

# Create virtual environment and install dependencies
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e .

Using Docker (recommended for testing)

Docker provides an isolated environment with both the Datastore emulator and MCP server pre-configured.

# Run tests
make test

# Start all services (emulator + server)
make up

# View logs
make logs

# Stop services
make down

For detailed Docker documentation, see DOCKER.md.

Configuration

The server can be configured using environment variables or command-line arguments.

Environment Variables

For Emulator:

DATASTORE_DATASET - Dataset name (default: test)
DATASTORE_EMULATOR_HOST - Datastore emulator host (default: localhost:8081)
DATASTORE_EMULATOR_HOST_PATH - Emulator host path (default: localhost:8081/datastore)
DATASTORE_HOST - Datastore HTTP host (default: http://localhost:8081)
DATASTORE_PROJECT_ID - Google Cloud project ID (default: test)
DATASTORE_NAMESPACE - Default namespace (optional)

For Production:

DATASTORE_PROJECT_ID - Google Cloud project ID (required)
GOOGLE_APPLICATION_CREDENTIALS - Path to service account key file (required)
DATASTORE_NAMESPACE - Default namespace (optional)

Running with Datastore Emulator (Default)

# Start the Datastore emulator (in a separate terminal)
gcloud beta emulators datastore start --host-port=localhost:8081

# Run the MCP server (uses emulator by default)
python src/datastore_mcp/server.py

# Or with custom emulator host
DATASTORE_EMULATOR_HOST=localhost:9090 python src/datastore_mcp/server.py

Running with Production Datastore

# Set credentials and project
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account-key.json
export DATASTORE_PROJECT_ID=your-gcp-project-id

# Unset emulator host to use production
unset DATASTORE_EMULATOR_HOST

# Run the server
python src/datastore_mcp/server.py

Usage with Claude Desktop

Add this configuration to your Claude Desktop config file:

MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%/Claude/claude_desktop_config.json

For Emulator (Development)

{
  "mcpServers": {
    "datastore": {
      "command": "python",
      "args": ["/path/to/datastore-mcp/src/datastore_mcp/server.py"],
      "env": {
        "DATASTORE_DATASET": "test",
        "DATASTORE_EMULATOR_HOST": "localhost:8081",
        "DATASTORE_EMULATOR_HOST_PATH": "localhost:8081/datastore",
        "DATASTORE_HOST": "http://localhost:8081",
        "DATASTORE_PROJECT_ID": "test"
      }
    }
  }
}

For Production

{
  "mcpServers": {
    "datastore": {
      "command": "python",
      "args": ["/path/to/datastore-mcp/src/datastore_mcp/server.py"],
      "env": {
        "DATASTORE_PROJECT_ID": "your-gcp-project-id",
        "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/service-account-key.json"
      }
    }
  }
}

Using Docker with Claude Desktop (Recommended)

Option 1: Fully Automated (Emulator + Server)

Use the provided wrapper script (included in the repository):

MacOS/Linux: start-datastore-mcp.sh Windows: start-datastore-mcp.bat

Then configure Claude Desktop:

MacOS/Linux:

{
  "mcpServers": {
    "datastore": {
      "command": "/path/to/datastore-mcp/start-datastore-mcp.sh"
    }
  }
}

Windows:

{
  "mcpServers": {
    "datastore": {
      "command": "C:\\path\\to\\datastore-mcp\\start-datastore-mcp.bat"
    }
  }
}

This option automatically starts the emulator if it's not running and waits for it to be healthy before starting the MCP server.

Option 2: Manual Emulator Start

First, start the Datastore emulator once:

cd /path/to/datastore-mcp
make emulator-only  # Keeps running in background

Then configure Claude Desktop:

{
  "mcpServers": {
    "datastore": {
      "command": "docker",
      "args": [
        "compose",
        "-f",
        "/path/to/datastore-mcp/docker-compose.yml",
        "run",
        "--rm",
        "mcp-server"
      ]
    }
  }
}

Option 3: External Emulator (Custom IP)

If your emulator runs on a different machine or custom IP:

{
  "mcpServers": {
    "datastore": {
      "command": "docker",
      "args": [
        "compose",
        "-f",
        "/path/to/datastore-mcp/docker-compose.yml",
        "run",
        "--rm",
        "-e", "DATASTORE_EMULATOR_HOST=localhost:8081",
        "-e", "DATASTORE_EMULATOR_HOST_PATH=localhost:8081/datastore",
        "-e", "DATASTORE_HOST=http://localhost:8081",
        "mcp-server"
      ]
    }
  }
}

Using uv with Claude Desktop

{
  "mcpServers": {
    "datastore": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/datastore-mcp",
        "run",
        "datastore-mcp"
      ],
      "env": {
        "DATASTORE_DATASET": "test",
        "DATASTORE_EMULATOR_HOST": "localhost:8081",
        "DATASTORE_EMULATOR_HOST_PATH": "localhost:8081/datastore",
        "DATASTORE_HOST": "http://localhost:8081",
        "DATASTORE_PROJECT_ID": "test"
      }
    }
  }
}

Available Tools

Once connected, the following tools are available to Claude:

datastore_get - Retrieve an entity by key
datastore_put - Create or update an entity
datastore_delete - Delete an entity
datastore_query - Query entities with filters and ordering
datastore_batch_get - Retrieve multiple entities by keys
datastore_list_kinds - List all entity kinds in the namespace

Example Queries

Ask Claude to:

"Get the User entity with ID 12345"
"Query all Products where price > 100, ordered by name"
"Create a new BlogPost entity with title and content"
"Delete the Comment entity with ID abc123"
"List all entity kinds in my datastore"

Development

Using Docker (Recommended)

# Run tests
make test

# Run tests with coverage
docker-compose run --rm test

# Start emulator only for local development
make emulator-only

# Interactive shell in container
make shell

Using Local Python

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=src/datastore_mcp --cov-report=term-missing

# Run the server
python src/datastore_mcp/server.py

Project Structure

datastore-mcp/
├── src/
│   └── datastore_mcp/
│       ├── server.py          # Main MCP server
│       ├── datastore.py       # Datastore client wrapper
│       └── tools.py           # Tool implementations
├── tests/
│   └── test_tools.py
├── pyproject.toml
└── README.md

License

MIT License

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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