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MCP in Practice: Cursor + Google MCP Tools

MCP in Practice: Cursor + Google MCP Tools

The Tools I'm Currently Using: Cursor + Google MCP

In my own workflow, I'm using Cursor (the AI coding IDE) combined with Google's MCP tooling.

This combination creates a powerful AI agent that can both understand my code and interact with my cloud environment.

Cursor

Cursor already integrates an MCP client under the hood.

When I run AI commands inside my codebase, it uses MCP to:

  • Inspect files: Read and understand code structure
  • Run terminals: Execute commands safely
  • Apply changes: Make code modifications
  • Search the project: Find relevant code and documentation
  • Refactor code safely: Understand context before making changes

The Feedback Loop

This creates a smooth feedback loop where the LLM has structured access to my environment—not unlimited access, but controlled, permissioned tools.

The AI can:

  • Understand the full context of my project
  • Make informed decisions based on code structure
  • Apply changes safely with proper validation
  • Learn from the codebase patterns

Google MCP Tools

Google's MCP servers (Firebase, Analytics, Data Commons, etc.) extend this ability beyond the local environment.

Available Google MCP Tools

  • Firebase MCP: Interact with Firebase projects and resources
  • Analytics MCP: Access Google Analytics data
  • Data Commons MCP: Query public datasets
  • Cloud Resources: Manage Google Cloud resources

What This Enables

This lets me:

  • Pull real analytics data directly into my dev process
  • Query project information from Google services
  • Access cloud resources programmatically
  • Integrate AI agents with Google services
  • Build AI-driven workflows that read/write real data

The Combined Power

Together, Cursor + Google MCP give me an AI agent that can:

Local Development

  • Understand my codebase structure
  • Make intelligent code changes
  • Run tests and validations
  • Refactor safely

Cloud Integration

  • Access real production data
  • Query analytics and metrics
  • Manage cloud resources
  • Build data-driven workflows

Example Workflow

  1. AI analyzes code: "I see you're using Firebase. Let me check your current Firebase configuration."
  2. AI queries Firebase MCP: Retrieves project settings and resources
  3. AI suggests improvements: Based on both code and actual cloud state
  4. AI applies changes: Safely modifies code with proper validation
  5. AI verifies: Checks that changes work with current cloud setup

This is far more powerful than traditional AI assistants that just generate text.

Real-World Benefits

Development Speed

  • Faster code understanding and modification
  • Quick access to cloud state and metrics
  • Reduced context switching

Code Quality

  • AI understands full context (local + cloud)
  • Better suggestions based on actual infrastructure
  • Safer changes with validation

Workflow Integration

  • Seamless integration between local and cloud
  • AI can work across the entire stack
  • Consistent interface for all operations

Getting Started

To use MCP tools in your workflow:

  1. Install Cursor: Get the AI-powered IDE
  2. Set up Google MCP: Configure Google MCP servers
  3. Define your tools: Expose your backend capabilities as MCP tools
  4. Start building: Let AI help with development and operations
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