TL;DR

The Firebolt MCP Server enables seamless integration between Firebolt cloud data warehouses and AI tools like Claude or Copilot via the Model Context Protocol (MCP). It allows LLMs to securely query data, explore schemas, and access documentation, streamlining tasks like SQL generation and code automation. This unlocks faster, smarter workflows and paves the way for autonomous AI agents to perform high-speed, data-driven research directly within Firebolt. Click here for access to the GitHub repo.

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As data engineers, we constantly seek ways to streamline workflows, optimize query performance, and accelerate the path from raw data to actionable insights within our cloud data warehouse environments. Today, we’re excited to introduce a powerful new way to do just that: the Firebolt MCP Server — a bridge between your Firebolt cloud data warehouse and the AI tools you already use, like Claude, Copilot, Cursor, and others.

This new offering implements the Model Context Protocol (MCP), an open standard designed to securely and effectively connect Large Language Models (LLMs) to diverse data sources and tools. Think of MCP as a standardized API layer for AI, enabling seamless, contextual communication between language models and external systems — including your Firebolt environment.

With the Firebolt MCP Server, LLMs can go beyond general-purpose tasks to perform specific, high-value interactions directly with your Firebolt databases. Whether you’re querying data, exploring schemas, or building intelligent assistants, this is a major step forward in making AI a first-class citizen in your data engineering workflows.

Why MCP? Standardizing LLM Tool Interaction

Before MCP, integrating LLMs with specific tools often required bespoke, complex solutions. The MCP (Model Context Protocol) introduces a standardized interface, simplifying how AI models discover and utilize available capabilities. For data engineers using Firebolt, this translates to a secure, reliable way to grant LLMs controlled access to specific functionalities.

The Firebolt MCP Server exposes a curated set of tools tailored for data engineering workflows:

1. firebolt_docs: Provides the LLM with direct, programmatic access to Firebolt's official documentation, including SQL reference, function definitions, data type specifications, and architectural guides. This ensures the LLM uses accurate, up-to-date information when assisting with syntax or concepts.
2. firebolt_connect: Allows the LLM to discover the user's accessible Firebolt environment, listing available accounts, databases, and compute engines. This is crucial for contextual awareness before attempting operations.
3. firebolt_query: Enables the LLM to execute SQL queries directly against a specified Firebolt database using an appropriate engine. The server manages secure connections and returns results to the LLM.

Authentication is handled securely via Firebolt service accounts (client ID and secret), ensuring credentials are never stored persistently by the server and access adheres to configured permissions. The server itself can be run easily via Docker or as a standalone binary.

Practical Applications for Data Engineers: AI-Accelerated Workflows

Let's move beyond the theoretical and explore concrete ways the Firebolt MCP Server, powering an LLM like Claude, can become an indispensable part of your toolkit.

1. Context-Aware Documentation Lookup

Navigating extensive documentation during development or troubleshooting can interrupt flow. The MCP server allows the LLM to fetch precise technical information on demand.

Technical Flow: A data engineer encounters an unfamiliar Firebolt function like ST_S2CELLIDFROMPOINT or needs the exact syntax for CREATE EXTERNAL TABLE with specific partitioning. They ask the LLM directly, "What is the syntax and behavior of ST_S2CELLIDFROMPOINT in Firebolt?" or "Show me the DDL for creating a partitioned external table reading Parquet files from S3." The LLM utilizes the firebolt_docs tool, performs a targeted search within the embedded documentation resources, extracts the relevant syntax, parameter descriptions, return types, and usage examples, and presents them directly to the engineer.

2. Natural Language to High-Performance SQL

While proficient SQL is essential, translating complex business questions into optimized queries takes time. MCP enables LLMs to act as intelligent translators, bridging natural language requirements with Firebolt's SQL dialect.

Technical Flow: A user asks, "Show me the 7-day rolling average of user sign-ups partitioned by acquisition channel for the last 90 days." The LLM, using its context about available tables (like users, acquisition_logs), formulates a Firebolt SQL query. This query might involve window functions (AVG() OVER(...)), date functions (DATE_TRUNC, DATE_DIFF), and joins. The LLM then invokes the firebolt_query tool via the MCP server, which executes the query against the relevant Firebolt engine and returns the structured results. The LLM can then format this result for readability.

3. Accelerated Client Code Generation

Integrating Firebolt queries into applications or scripts often involves repetitive boilerplate code for establishing connections, executing queries, and handling results.

Technical Flow: An engineer needs to write a Python script to periodically fetch data from a Firebolt table. They ask the LLM, "Generate Python code using the Firebolt SDK to connect with service account credentials, run 'SELECT * FROM daily_metrics WHERE event_date = CURRENT_DATE', and fetch results into a Pandas DataFrame." The LLM generates the Python code, importing necessary libraries (like firebolt-sdk), setting up the connection using the correct authentication parameters, executing the query, fetching results, and potentially including basic error handling. It might implicitly use best practices learned from documentation accessed via firebolt_docs.

Getting Started with Firebolt MCP Server

Integrating this powerful AI capability into your workflow is straightforward:

1. Prerequisites: Ensure you have a Firebolt service account with its client ID and secret.
2. Installation: Deploy the Firebolt MCP Server using the provided Docker image or download the appropriate binary for your environment. Pass your credentials securely as environment variables or command-line arguments.

Example using Docker
docker run --rm -i \
-e FIREBOLT_MCP_CLIENT_ID="your-client-id" \
-e FIREBOLT_MCP_CLIENT_SECRET="your-client-secret" \
ghcr.io/firebolt-db/mcp-server:latest

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1. LLM Client Configuration: Configure your preferred MCP-compatible client (e.g., Claude Desktop, GitHub Copilot Chat in VSCode, Cursor editor) to connect to the running MCP server instance. Refer to the MCP Server README and client-specific documentation for detailed steps.
2. Engage: Start interacting with your Firebolt data warehouse through your AI assistant!

Expanding Horizons: Enabling Autonomous AI Agents with High-Speed Analytics

While the above use cases significantly enhance data engineering productivity, the combination of MCP and Firebolt unlocks a more profound potential: enabling autonomous AI agents capable of conducting complex, data-driven research at machine speed.

Imagine an AI research agent tasked with uncovering subtle correlations within petabytes of IoT sensor data or web traffic data stored in Firebolt. MCP provides the crucial interface for this agent to interact with the data warehouse autonomously:

• Self-Directed Exploration: The agent can understand the available datasets and schemas within Firebolt.
• Hypothesis Generation & Testing: Based on its objectives and initial data exploration, the agent formulates hypotheses. It then designs and executes sequences of complex SQL queries via Firebolt MCP Server to test these hypotheses against massive datasets.
• Iterative Refinement: The agent analyzes the query results returned through MCP. Critically, Firebolt's ultra-fast query performance is essential here. Sub-second response times on terabyte-scale datasets allow the agent to iterate rapidly – adjusting hypotheses, formulating new queries, and diving deeper into the data far faster than any human analyst could.   
• Knowledge Synthesis: The agent leverages Firebolt documentation to ensure its generated queries are syntactically correct and utilize Firebolt features optimally (e.g., geospatial functions, array processing, index utilization). It integrates findings from multiple queries to build a comprehensive understanding.  

This autonomous loop – explore, hypothesize, query, analyze, refine – powered by the MCP interface and accelerated by Firebolt's high-performance analytics engine, transforms the scale and speed at which data-driven research can occur. The agent isn't just retrieving data; it's performing in silico experiments, discovering patterns, and generating novel insights directly from the underlying data warehouse, pushing the boundaries of automated scientific discovery and business intelligence.

Our release of the Firebolt MCP Server is just the beginning. We're closely following the latest advancements in AI and working on new features specifically designed to power the next generation of data and AI applications. Whether you're building analytics pipelines, creating intelligent data products, or enabling AI agents to autonomously explore your data warehouse, Firebolt is the high-performance foundation you need.

We’re excited about what’s next. Stay tuned — more powerful capabilities are on the way.