Play Store

The Play Store MCP Server connects your MCP clients with Google Play Console to automate Android application deployments and management.

This allows you to deploy apps, promote releases between tracks, and monitor deployment status directly through conversational AI.

Features

  • πŸš€ Automated App Deployment: Upload APK/AAB files directly to Google Play Console
  • πŸ“Š Release Track Management: Move apps between internal, alpha, beta, and production tracks
  • 🎯 Gradual Rollouts: Control release percentage for staged deployments
  • πŸ“‹ Release Status Queries: Check current deployment status across all tracks
  • πŸ”§ Version Code Management: Handle version promotion with proper validation
  • πŸ“ Release Notes Integration: Include release notes with deployments

Use Cases

  • CI/CD Pipeline Integration: Automate the final deployment step in your build process by having Claude push releases to Play Store after successful builds and tests
  • Release Management Workflows: QA teams can easily promote builds from internal testing to alpha, then beta, and finally production without manual Console navigation
  • Gradual Rollout Strategy: Marketing teams can deploy updates to small user percentages first, monitor metrics, then expand rollouts based on performance data
  • Multi-App Portfolio Management: Development studios managing multiple Android apps can query release status and coordinate deployments across their entire app portfolio through natural language commands

How to Use It

  • 1. Google Cloud and Play Console Setup

Create a Google Cloud Console project and navigate to the APIs & Services Library. Search for “Google Play Android Developer API” and enable it for your project.

Set up a service account by going to IAM & Admin β†’ Service Accounts. Create a new service account named play-store-mcp with a description like “Service account for Play Store MCP operations.” You don’t need to assign project-level roles during creation.

Generate a JSON key for the service account. Click on the service account email, go to the Keys tab, and create a new JSON key. Download and securely store this fileβ€”you’ll need its path for configuration.

Configure Play Console permissions by visiting Google Play Console and going to Users and Permissions. Invite the service account email as a new user and grant it “View app information” permissions plus all permissions under the “Versions” section. You’ll also need to specify which apps this service account can manage.

2. Add this configuration to your MCP settings file:

{
  "mcpServers": {
    "play-store-mcp": {
      "command": "java",
      "args": [
        "-jar",
        "/path/to/play-store-mcp-all.jar"
      ],
      "env": {
        "PLAY_STORE_SERVICE_ACCOUNT_KEY_PATH": "/path/to/service-account-key.json",
        "PLAY_STORE_DEFAULT_TRACK": "internal"
      }
    }
  }
}

3. Environment Variables

  • PLAY_STORE_SERVICE_ACCOUNT_KEY_PATH: Full path to your service account JSON file (required)
  • PLAY_STORE_DEFAULT_TRACK: Default deployment track, typically “internal” (optional)

4. Download the JAR file from the GitHub releases page, or build it yourself:

./gradlew clean build -x test

This generates build/libs/play-store-mcp-all.jar which you reference in your MCP configuration.

5. Available Tools:

deploy_app: Uploads new app versions to Play Store

  • packageName: App package identifier (com.example.myapp)
  • track: Target track (internal, alpha, beta, production)
  • apkPath: Path to your APK or AAB file
  • versionCode: New version code (must exceed current version)
  • releaseNotes: Optional release notes text
  • rolloutPercentage: Rollout percentage from 0.0 to 1.0 (default: 1.0)

promote_release: Moves existing versions between tracks

  • packageName: App package identifier
  • fromTrack: Source track (internal, alpha, beta)
  • toTrack: Destination track (alpha, beta, production)
  • versionCode: Version code to promote

get_releases: Queries current release status

  • packageName: App package identifier

6. Usage Examples

Check current app releases:

“Show me the current release status for com.example.myapp”

Deploy a new version with gradual rollout:

“Deploy version 142 of com.example.myapp to alpha track with 30% rollout using the release notes from notes.txt”

Promote a beta release to production:

“Promote version 141 of com.example.myapp from beta to production”

FAQs

Q: Can I use this with apps that haven’t been published yet?
A: No, apps must already exist in Play Console, even as drafts. The server can’t create new app listingsβ€”it only manages existing ones.

Q: What file formats does the server support?
A: Both APK and AAB files are supported. Google recommends AAB format for new uploads as it enables Play Feature Delivery and smaller download sizes.

Q: Why am I getting “The caller does not have permission” errors?
A: This usually means the service account lacks proper permissions in Play Console. Double-check that you’ve granted “View app information” and all “Versions” permissions, and that you’ve specified which apps the account can access.

Q: Can I roll back a deployment if issues are found?
A: The server doesn’t include rollback functionality yet. You’d need to manually halt the rollout in Play Console or deploy a new version with fixes.

Q: Does this work with internal app sharing or just official tracks?
A: The server works with the four official tracks: internal, alpha, beta, and production. Internal app sharing is a separate Play Console feature not supported by this server.

Q: What happens if I try to deploy a version code that already exists?
A: Google Play requires version codes to be strictly increasing. The deployment will fail if you try to upload a version code that’s not higher than the current version.

Latest MCP Servers

Notion

Notion's official MCP Server allows you to interact with Notion workspaces through the Notion API.

Log Mcp

An MCP server that provides 7 tools for log analysis, including error fingerprinting, pattern comparison, and ML classification.

Apple

An MCP package that provides AI assistants with direct access to Notes, Messages, Mail, Contacts, Reminders, Calendar, and Maps via AppleScript and EventKit.

View More MCP Servers >>

Featured MCP Servers

Notion

Notion's official MCP Server allows you to interact with Notion workspaces through the Notion API.

Claude Peers

An MCP server that enables Claude Code instances to discover each other and exchange messages instantly via a local broker daemon with SQLite persistence.

Excalidraw

Excalidraw's official MCP server that streams interactive hand-drawn diagrams to Claude, ChatGPT, and VS Code with smooth camera control and fullscreen editing.

More Featured MCP Servers >>

FAQs

Q: What exactly is the Model Context Protocol (MCP)?

A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.

Q: How is MCP different from OpenAI's function calling or plugins?

A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.

Q: Can I use MCP with frameworks like LangChain?

A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.

Q: Why was MCP created? What problem does it solve?

A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.

Q: Is MCP secure? What are the main risks?

A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.

Q: Who is behind MCP?

A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.

Get the latest & top AI tools sent directly to your email.

Subscribe now to explore the latest & top AI tools and resources, all in one convenient newsletter. No spam, we promise!