Firebase MCP Server
The Firebase Model Context Protocol (MCP) Server gives AI-powered development tools the ability to work with your Firebase projects and your app's codebase. The Firebase MCP server works with any tool that can act as an MCP client, including: Firebase Studio, Gemini CLI, Claude Code, Cline, Cursor, VS Code Copilot, Windsurf, and more! An editor configured to use the Firebase MCP server can use its AI capabilities to help you: - Create and manage Firebase projects - Initialize new projects, list existing ones, and manage Firebase apps - Manage Firebase Authentication users - Retrieve, update, and manage user accounts - Work with Cloud Firestore and Firebase SQL Connect - Query, read, write, and manage database documents - Retrieve Firebase SQL Connect schemas - Generate schemas and operations with AI assistance - Understand security rules - Validate and retrieve security rules for Firestore, Cloud Storage, and Realtime Database - Send messages with Firebase Cloud Messaging - Send push n
Ads
Overview
The Firebase Model Context Protocol (MCP) Server gives AI-powered development tools the ability to work with your Firebase projects and your app's codebase. The Firebase MCP server works with any tool that can act as an MCP client, including: Firebase Studio, Gemini CLI, Claude Code, Cline, Cursor, VS Code Copilot, Windsurf, and more! An editor configured to use the Firebase MCP server can use its AI capabilities to help you: - Create and manage Firebase projects - Initialize new projects, list existing ones, and manage Firebase apps - Manage Firebase Authentication users - Retrieve, update, and manage user accounts - Work with Cloud Firestore and Firebase SQL Connect - Query, read, write, and manage database documents - Retrieve Firebase SQL Connect schemas - Generate schemas and operations with AI assistance - Understand security rules - Validate and retrieve security rules for Firestore, Cloud Storage, and Realtime Database - Send messages with Firebase Cloud Messaging - Send push n
Capabilities
- firebase_create_project
Create and manage Firebase projects — Ask the assistant to initialize a new project with `firebase_create_project` or list existing projects and apps.
- auth_get_users
Manage Authentication users — Retrieve user details with `auth_get_users`, update custom claims, or disable accounts via `auth_update_user`.
- firebase_read_resources
Query and update Firestore or Realtime Database — Read, write, and manage documents using `firebase_read_resources` and database-related tools.
- crashlytics_get_issue
Debug crashes with Crashlytics — Fetch issue details, list recent crash events, and add notes using `crashlytics_get_issue` and `crashlytics_list_events`.
- firebase_validate_security_rules
Validate and retrieve security rules — Check Firestore, Storage, or Realtime Database rules for errors with `firebase_validate_security_rules`.
- apphosting_list_backends
Monitor App Hosting backends — List backends via `apphosting_list_backends` and fetch recent build or runtime logs with `apphosting_fetch_logs`.
Installation
claude mcp add firebase npx -- -y firebase-tools@latest mcp
Configuration
{
"mcpServers": {
"firebase-mcp-server": {
"url": "https://cursor.com/deeplink/mcp"
}
}
}Paste into ~/.cursor/mcp.json or .cursor/mcp.json
Tags
- development
Related MCP servers
Code Execution
Context7 MCP
LLMs rely on outdated or generic information about the libraries you use. You get: - ❌ Code examples are outdated and based on year-old training data - ❌ Hallucinated APIs that don't even exist - ❌ Generic answers for old package versions Context7 pulls up-to-date, version-specific documentation and code examples straight from the source — and places them directly into your prompt. Context7 fetches up-to-date code examples and documentation right into your LLM's context. No tab-switching, no hallucinated APIs that don't exist, no outdated code generation.
5 tools · Fetch current docs, Get real code examples
Code Execution
Chrome DevTools MCP
Chrome DevTools for agents (chrome-devtools-mcp) lets your coding agent (such as Antigravity, Claude, Cursor or Copilot) control and inspect a live Chrome browser. It acts as a Model-Context-Protocol (MCP) server, giving your AI coding assistant access to the full power of Chrome DevTools for reliable automation, in-depth debugging, and performance analysis. A CLI is also provided for use without MCP. - Get performance insights: Uses [Chrome DevTools](https://github.com/ChromeDevTools/devtools-frontend) to record traces and extract actionable performance insights. - Advanced browser debugging: Analyze network requests, take screenshots and check browser console messages (with source-mapped stack traces). - Reliable automation. Uses puppeteer to automate actions in Chrome and automatically wait for action results.
6 tools · click, lighthouse_audit
Code Execution
Mastra/mcp
Mastra supports the Model Context Protocol (MCP), an open standard for connecting AI agents to external tools and resources. It serves as a universal plugin system, enabling agents to call tools regardless of language or hosting environment. Mastra can also be used to author MCP servers, exposing agents, tools, and other structured resources via the MCP interface. These can then be accessed by any system or agent that supports the protocol. To use MCP, install the required dependency: Mastra currently supports two MCP classes:
4 tools · Connect agents to external MCP servers, Expose Mastra agents via MCP
Code Execution
GitHub MCP Server
Connect your AI assistant to GitHub repositories, issues, pull requests, and search.
4 tools · create_issue, get_pull_request
Code Execution
Kubeshark
Kubeshark MCP (Model Context Protocol) server enables AI assistants like Claude Desktop, Cursor, and other MCP-compatible clients to query real-time Kubernetes network traffic. The MCP provides the tools — AI skills teach agents how to use them. Skills turn raw MCP capabilities into domain-specific workflows like root cause analysis, traffic filtering, and forensic investigation. See the skills README for installation and usage. - L7 API Traffic Analysis: Query HTTP, gRPC, Redis, Kafka, DNS transactions - L4 Network Flows: View TCP/UDP flows with traffic statistics - Cluster Management: Start/stop Kubeshark deployments (with safety controls) - PCAP Snapshots: Create and export network captures - Built-in Prompts: Pre-configured prompts for common analysis tasks
6 tools · create_snapshot, enable_dissection
Code Execution
AWS Nova Canvas
Generate images using Amazon Nova Canvas with text prompts and color guidance.
Code Execution
Lingo.dev
Open-source localization engineering tools. Connect to Lingo.dev localization engineering platform for consistent, quality translations. Lingo API • Lingo React MCP • Lingo CLI • Lingo GitHub Action • Lingo Compiler for React (Early alpha) These tools connect to localization engines – stateful translation APIs you create on the Lingo.dev localization engineering platform. Each engine persists glossaries, brand voice, and per-locale instructions across every request, reducing terminology errors 16.6–44.6%. Or bring your own LLM.
3 tools · Prevent hallucinated APIs, Set up i18n in React apps
Code Execution
Google MCP Servers
This repository contains a list of Google's official Model Context Protocol (MCP) servers, guidance on how to deploy MCP servers to Google Cloud, and examples to get started. These remote MCP servers are managed by Google, and are available via endpoint. Below are a few key remote servrs. For a comprehensive list of all remote MCP servers managed by Google in General Availability (GA) and public preview vist the supported products pager AlloyDB for PostgreSQL BigQuery Bigtable Cloud Resource Manager Cloud SQL for MySQL Cloud SQL for PostgreSQL Cloud SQL for SQL Server Compute Engine (GCE) Developer Knowledge API (Google Developer Documentation) Firestore Google Maps (Grounding Lite) Google Security Operations (Chronicle) Kubernetes Engine (GKE) Spanner Cloud Run (GA) Cloud Storage
6 tools · Access Google developer documentation, Generate images and video