
Firebase Docs MCP Server Setup
This is a sample for showing how to do FIrebase Docs as an MCP server (including indexing documents)
What is Firebase Docs MCP?
Firebase Docs MCP is a server setup project that demonstrates how to index Firebase documentation using the Model Context Protocol (MCP). It includes components for indexing documents and serving them over a protocol server.
How to use Firebase Docs MCP?
To use Firebase Docs MCP, you need to set up an API key, index the Firebase documents, and then test the server using the provided commands in the documentation.
Key features of Firebase Docs MCP?
- Indexing Firebase documentation into markdown format.
- SQL lite database integration for indexed documents.
- Model Context Protocol server for serving indexed content.
Use cases of Firebase Docs MCP?
- Indexing and serving Firebase documentation for easy access.
- Testing the MCP server with various tools and requests.
- Integrating Firebase documentation into applications using the MCP server.
FAQ from Firebase Docs MCP?
- What is the purpose of the API key?
The API key is required to access the Gemini embedding model used for indexing documents.
- How do I handle indexing failures?
The project includes a retry strategy to reindex documents if the initial indexing fails.
- Can I test the server locally?
Yes! You can test the server locally by following the provided commands in the documentation.
Firebase Docs MCP Server Setup
Directory Layout
docs-mcp
This corresponds to the indexer for Firebaes docs. This is a Go project that goes and indexes the Firebase documents contained within the listed filepaths.
docs-mcp-server
This is the model context protocol server that serves content over a stdio transport.
genkit-mcp-tester
This is a genkit implementation of an MCP client to test using the docs-mcp-server.
How to use
Start with indexing
-
Set the API Key. We are using the Gemini embedding model for the documents so getting an API key from AI Studio is required. To set the API key, call
export genaikey="APIKEY"
in your terminal -
Ensure that the output directory is empty. We are writing files to your home directory in a folder called
.indexResp
. As go fetches documents from the Firebase documentation site, it writes the files to disk in markdown format and also indexes them in a SQL lite database in this directory. If indexing fails, it performes a retry strategy to reindex the documents into a markdown format. -
From the
docs-mcp
folder, callgo run .
This will start the indexing process on the files listed near line 291 in themain.go
file.
Test the indexer
-
Set the API Key. We are using the Gemini embedding model for the documents so getting an API key from AI Studio is required. To set the API key, call
export genaikey="APIKEY"
in your terminal -
Switch into the
docs-mcp-server
folder. -
Copy the indexed database to the local
docs-mcp-server
folder. This can be done by callingcp $HOME/.indexResp/db.sqlite .
-
Install the dependencies and build the project.
npm ci
and thennpm run build
. Once the project is built, you can then test the project by callingnpm run build && npx @modelcontextprotocol/inspector node build/index.js
. This starts the inspector and should print a URL for you to view the STDIO server with. -
Click on Connect in the inspector view, and then click on tools -> List Tools -> find-firebase-doc and then type in for your request that you would want to use. NOTE: The author has had trouble using the terminal built into VSCode for running this step, so if you run into a similar issue, try the system terminal.
Use Genkit for testing
-
Set the API key in the code by changing this line in embedding.ts from :
const genAiKey = process.env.genaikey || "";
toconst genAiKey = process.env.genaikey || "MYAPIKEY";
-
Switch into the
genkit-mcp-tester
directory. -
Copy the indexed database to the local
genkit-mcp-tester
folder. This can be done by callingcp $HOME/.indexResp/db.sqlite .
-
Install the dependencies and build the project.
npm ci
and thennpm run build
. Once the project is built, you can then test the project by callingnpx genkit start -- npx tsx --watch src/index.ts
. This starts the Genkit DevUI where you can interact with the flow and tool directly. Open the DevUI, generally http://localhost:4000 and visit the Tools ->find-firebase-doc/find-firebase-doc
tool and make a request here. You can see that the request is then returning the results we see in the modelcontextprotocol/inspector.