Django Firebase MCP
A Django application that implements a Firebase Model Context Protocol (MCP) server, enabling AI agents to interact with Firebase services through a standardized protocol and providing 14 tools such as authentication, database, and storage.
rating : 2 points
downloads : 6.3K
What is the Django Firebase MCP Server?
The Django Firebase MCP server is a specially built system that allows AI agents to interact with Firebase services through a standardized protocol. It provides a complete set of tools and interfaces, enabling developers to easily integrate Firebase features into their AI applications.How to use the Django Firebase MCP Server?
To use the Django Firebase MCP server, you need to set up a Firebase project and configure the necessary environment variables first. Then, you can start the server by running the provided script and interact with Firebase services using the commands and APIs it offers.Use Cases
This server is suitable for AI applications that need to integrate with Firebase services, such as user authentication, data storage, and file management. It is particularly suitable for developers who want to quickly implement functions for interacting with Firebase.Main Features
Firebase Authentication IntegrationSupports verifying Firebase ID tokens, creating custom authentication tokens, and retrieving and deleting user information.
Firestore Database OperationsProvides functions for listing collections, creating, retrieving, updating, and deleting documents, as well as queries with filtering conditions.
Cloud Storage ManagementSupports file upload, download, deletion, and listing, facilitating the handling of files in cloud storage.
State ManagementSupports saving in Redis or memory for maintaining session states and persisting data.
Easy IntegrationCan be easily integrated into existing Django projects and provides an independent test mode for the agent.
Advantages and Limitations
Advantages
Provides a standard protocol for easy interaction between AI agents and Firebase services
Supports multiple Firebase functions, including authentication, database, and storage
Easy to integrate into existing projects, suitable for rapid development
Supports state management to ensure session persistence
Limitations
Requires a Firebase project and relevant credentials, and the initial setup is relatively complex
May require a certain learning curve for non-technical users
Requires additional deployment and maintenance when relying on Redis
How to Use
Clone the Project
Clone the code repository of the Django Firebase MCP server from GitHub.
Install Dependencies
Install the required Python packages in the project directory.
Configure Firebase
Create a project in the Firebase console and obtain the service account key file. Save it as credentials.json.
Set Environment Variables
Create a .env file and fill in the configuration information related to Firebase and MCP.
Start the Server
Run the Django development server to start the MCP service.
Usage Examples
Interaction between AI Agent and Firebase DatabaseAn AI agent interacts with the Firebase Firestore database through the MCP protocol to perform data query and update operations.
File Upload and ManagementAn AI agent uses the MCP protocol to upload and manage files in cloud storage.
Frequently Asked Questions
How to solve the error that the default app does not exist?
What if the server fails to start?
What if the Firebase connection fails?
How to switch to Redis state management?
Related Resources
Firebase Official Documentation
The official documentation of Firebase, providing detailed service descriptions and usage guides.
Django Official Documentation
The official documentation of Django, providing comprehensive information about the Django framework.
GitHub Project Repository
The GitHub project repository of the Django Firebase MCP server.
MCP Protocol Specification
The detailed specification document of the Model Context Protocol (MCP).
Featured MCP Services

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
11.4K
4.5 points

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
10.6K
4.3 points

Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
25.7K
4.3 points

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
17.2K
5 points

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
29.6K
4.5 points

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#
13.4K
5 points

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
33.8K
4.7 points

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
11.5K
4.5 points