Firestore MCP
F

Firestore MCP

An MCP server for the Firebase Firestore database, supporting AI assistants to directly perform CRUD operations, queries, and collection management
2 points
7.2K

What is the Firestore MCP Server?

The Firestore MCP Server is a middleware that connects AI assistants and the Firebase Firestore database. It is built based on the Model Context Protocol (MCP) standard, allowing AI assistants like Claude to directly query, modify, and manage your Firestore database without writing code or manually operating the database interface.

How to use the Firestore MCP Server?

The usage is divided into three main steps: 1) Install and configure the server, and set up Firebase authentication information; 2) Configure the MCP server connection in Claude Code; 3) Use natural language instructions directly in the conversation to operate the database, such as 'List all users' or 'Create a new order'.

Applicable scenarios

It is suitable for scenarios where developers need to quickly query and test the database during development, product managers or operation personnel need to view business data, and customer service AIs need to access user information. It is especially suitable for workflows that require frequent interaction with the database but do not want to switch tools.

Main features

Complete CRUD operations
Supports all operations of creating (Create), reading (Read), updating (Update), and deleting (Delete) Firestore documents. AI can manage data like a developer.
Collection management
Can list all top - level collections and sub - collections of documents, helping AI understand the database structure and navigate to the correct data location.
Advanced query support
Supports a variety of Firestore query operators, such as equal to, not equal to, greater than, less than, array contains, etc., allowing AI to perform complex data filtering.
Document counting
Can count the number of documents in a collection without fetching all the data, which is suitable for quickly understanding the data scale and avoiding performance issues when dealing with large amounts of data.
Intelligent type conversion
Automatically handles special data types in Firestore (such as timestamps, geographical locations, etc.) and performs transparent conversion between AI and the database.
Bilingual support
Both the server and documents support English and Japanese, which is suitable for international teams. AI can operate the database in different languages.
Advantages
Operate the database without writing code, reducing the technical threshold
Natural language interaction, intuitive and easy to use
Support complex query and filtering operations
Automatically handle special data types in Firestore
Simple configuration, quickly integrate into the existing workflow
Support English and Chinese operations, suitable for international teams
Limitations
Requires Firebase service account permissions, and the configuration has certain technical requirements
Not suitable for direct use in the production environment, it is recommended to use only for development and testing
Query performance is limited by Firestore itself
Requires a client that supports MCP, such as Claude Code
Limited support for complex transaction operations

How to use

Environment preparation
Ensure that Node.js version 18+ is installed and you have a Firebase project with Firestore enabled.
Get Firebase authentication information
Generate a service account key from the Firebase console and obtain the project ID, client email, and private key.
Install and configure the server
Clone the repository, install dependencies, and configure the environment variable file.
Configure Claude Code
Create a .mcp.json file in the project root directory and configure the server path.
Start using
Restart Claude Code, approve the MCP server connection, and then use natural language to operate the database in the conversation.

Usage examples

User data query
Product managers need to view information about recently registered active users
Order data statistics
Operation personnel need to count the number of orders and the total amount today
Product inventory management
Inventory administrators need to update the status of out - of - stock products
User feedback analysis
Customer service supervisors need to view user feedback from the past week

Frequently Asked Questions

Is this tool safe? Will it accidentally delete data?
Do I need programming knowledge to use it?
What Firestore query operations are supported?
How to handle timestamp and geographical location data?
Will querying a large amount of data be slow?
Can it be shared and used in a team?

Related resources

Model Context Protocol official website
Official documentation and specifications of the MCP protocol
Firebase Console
Manage Firebase projects and obtain authentication information
Claude Code download
Claude desktop application supporting MCP
GitHub repository
Project source code and latest version
Firestore documentation
Official Firestore usage guide

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "firestore": {
      "command": "node",
      "args": ["/path/to/firestore-mcp/dist/index.js"]
    }
  }
}

{
  "mcpServers": {
    "firestore": {
      "command": "node",
      "args": ["/path/to/firestore-mcp/dist/index.js"],
      "env": {
        "FIREBASE_PROJECT_ID": "your-project-id",
        "FIREBASE_CLIENT_EMAIL": "your-client-email",
        "FIREBASE_PRIVATE_KEY": "-----BEGIN PRIVATE KEY-----\\n...\\n-----END PRIVATE KEY-----\\n"
      }
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

Alternatives

V
Vestige
Vestige is an AI memory engine based on cognitive science. By implementing 29 neuroscience modules such as prediction error gating, FSRS - 6 spaced repetition, and memory dreaming, it provides long - term memory capabilities for AI. It includes a 3D visualization dashboard and 21 MCP tools, runs completely locally, and does not require the cloud.
Rust
6.5K
4.5 points
M
Moltbrain
MoltBrain is a long-term memory layer plugin designed for OpenClaw, MoltBook, and Claude Code, capable of automatically learning and recalling project context, providing intelligent search, observation recording, analysis statistics, and persistent storage functions.
TypeScript
6.4K
4.5 points
B
Bm.md
A feature-rich Markdown typesetting tool that supports multiple style themes and platform adaptation, providing real-time editing preview, image export, and API integration capabilities
TypeScript
4.6K
5 points
S
Security Detections MCP
Security Detections MCP is a server based on the Model Context Protocol that allows LLMs to query a unified security detection rule database covering Sigma, Splunk ESCU, Elastic, and KQL formats. The latest version 3.0 is upgraded to an autonomous detection engineering platform that can automatically extract TTPs from threat intelligence, analyze coverage gaps, generate SIEM-native format detection rules, run tests, and verify. The project includes over 71 tools, 11 pre-built workflow prompts, and a knowledge graph system, supporting multiple SIEM platforms.
TypeScript
6.7K
4 points
P
Paperbanana
Python
7.0K
5 points
B
Better Icons
An MCP server and CLI tool that provides search and retrieval of over 200,000 icons, supports more than 150 icon libraries, and helps AI assistants and developers quickly obtain and use icons.
TypeScript
7.7K
4.5 points
A
Assistant Ui
assistant - ui is an open - source TypeScript/React library for quickly building production - grade AI chat interfaces, providing composable UI components, streaming responses, accessibility, etc., and supporting multiple AI backends and models.
TypeScript
7.8K
5 points
A
Apify MCP Server
The Apify MCP Server is a tool based on the Model Context Protocol (MCP) that allows AI assistants to extract data from websites such as social media, search engines, and e-commerce through thousands of ready-to-use crawlers, scrapers, and automation tools (Apify Actors). It supports OAuth and Skyfire proxy payment and can be integrated into MCP clients such as Claude and VS Code through HTTPS endpoints or local stdio.
TypeScript
6.7K
5 points
M
Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
36.2K
5 points
G
Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
25.1K
4.3 points
D
Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
74.2K
4.3 points
N
Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
20.8K
4.5 points
F
Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
65.9K
4.5 points
U
Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
32.2K
5 points
G
Gmail MCP Server
A Gmail automatic authentication MCP server designed for Claude Desktop, supporting Gmail management through natural language interaction, including complete functions such as sending emails, label management, and batch operations.
TypeScript
21.3K
4.5 points
C
Context7
Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.
TypeScript
98.0K
4.7 points
AIBase
Zhiqi Future, Your AI Solution Think Tank
© 2026AIBase