F

Firebase Docs MCP

This project is a Firebase documentation indexing and retrieval system, including a document indexer written in Go, a server based on the Model Context Protocol, and a Genkit client implementation for testing.
2 points
10

What is the Firebase Docs MCP Server?

This is an intelligent retrieval system specifically designed for Firebase documentation. It can automatically index the content of Firebase official documentation and help developers quickly find relevant documents through natural language queries. The system uses the Gemini model for semantic understanding to provide more accurate search results.

How to use the Firebase Docs MCP Server?

The usage process is divided into three main steps: 1) Set up the API key. 2) Index the documents. 3) Query the documents. The system provides multiple query methods, including command - line tools and a visual interface.

Use cases

It is particularly suitable for Firebase developers to use in the following scenarios: quickly finding documentation for specific functions, solving problems encountered in development, and obtaining relevant materials when learning new Firebase features.

Main features

Document indexingAutomatically crawl and index the content of Firebase official documentation and convert it into a searchable format
Semantic searchUse the Gemini model to understand the query intention and return the most relevant document fragments
Multi - platform supportProvide two query methods: command - line tools and a Web interface
Offline cachingThe indexed documents are stored in the local SQLite database, supporting offline queries

Advantages and limitations

Advantages
Accurate semantic search ability to understand developers' query intentions
Fast response speed, and local caching ensures query efficiency
Support multiple usage methods to flexibly adapt to different scenarios
Limitations
A Google AI Studio API key is required to use it
Document updates require re - indexing
Some complex queries may not be accurate enough

How to use

Get the API key
Get the API key for the Gemini model from Google AI Studio
Index the documents
Run the indexing program to crawl and process Firebase documentation
Start the server
Start the MCP server to prepare to receive query requests
Execute the query
Submit query requests through the command - line tool or the Web interface

Usage examples

Find authentication documentationDevelopers need to know how to set up the Firebase authentication function
Query storage pricingDevelopers want to know the billing method for the Firebase storage service

Frequently Asked Questions

Why is a Google AI Studio API key required?
How to update the indexed document content?
Why can't it run properly in the VSCode terminal?

Related resources

Firebase official documentation
Official documentation for all Firebase products
Google AI Studio
Get the API key for the Gemini model
Model Context Protocol specification
Technical specification of the MCP protocol
Installation
Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.
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