Datastore MCP Server

Datastore MCP Server

Enables AI assistants to interact with Google Cloud Datastore, allowing them to create, read, update, delete entities, and perform queries with filtering and ordering.

Category
Visit Server

README

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

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.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured