Firebase Docs MCP Server

Firebase Docs MCP Server

The Firebase Docs MCP Server transforms the extensive Firebase documentation into a searchable, AI‑ready knowledge base that can be queried directly by conversational assistants. By indexing Markdown representations of the official docs and storing vector embeddings in a lightweight SQLite database, it removes the need for external search engines or costly cloud services. Developers can now embed Firebase knowledge into Claude, Gemini, or other LLMs with a single tool invocation, enabling more accurate and contextually relevant responses about Firebase features, APIs, and best practices.

At its core, the server offers a single “find‑firebase-doc” tool that accepts natural language queries and returns the most relevant excerpts from the indexed Firebase docs. The underlying pipeline leverages Gemini embeddings to convert text into high‑dimensional vectors, which are then compared against the query vector using efficient similarity search. This means that even complex, multi‑step questions can be answered by pulling precise code snippets or configuration examples from the official Firebase documentation, all without leaving the assistant’s conversation flow.

Key capabilities include:

• Full‑text indexing of the entire Firebase docs site, preserving hierarchy and metadata.
• Incremental re‑indexing with retry logic to handle network hiccups or API rate limits.
• StdIO‑based MCP server that can be launched locally, making it ideal for on‑prem or CI environments.
• GenKit integration for rapid prototyping, allowing developers to test the tool inside a familiar flow builder and DevUI.
• SQLite persistence ensures fast startup times and low resource consumption compared to full‑text search engines.

Typical use cases span a wide range of developer workflows. A chatbot that assists new Firebase users can instantly fetch the exact “Create a Firestore collection” guide when asked. A code‑review assistant can pull relevant security rules examples during a review session. Even internal knowledge bases or documentation portals can expose Firebase content through the same tool, keeping all teams on a single source of truth. Because the server runs locally and relies only on open‑source components, it scales from a single developer machine to a distributed cloud deployment without additional cost.

What sets this MCP apart is its tight coupling with the Firebase ecosystem. The indexer pulls directly from the live docs site, ensuring that updates to Firebase are reflected in real time after a quick re‑run. Coupled with the GenKit tester, developers can iterate on conversational flows that include Firebase queries without leaving their IDE. In short, the Firebase Docs MCP Server turns static documentation into an interactive, AI‑powered resource that accelerates development and reduces friction for anyone working with Firebase.