Overview

The Cloudera Iceberg MCP Server provides a lightweight, read‑only interface for large language models to explore and query data stored in Apache Iceberg tables through Impala. By exposing a simple set of MCP endpoints— and —the server lets an AI assistant inspect database schemas, retrieve metadata, and run arbitrary SQL queries without risking data modification. This solves a common pain point for developers building data‑centric AI applications: how to give an LLM direct, safe access to a live data lake without exposing the underlying infrastructure or compromising security.

At its core, the server translates an LLM’s request into a standard Impala query, executes it against the configured database, and returns the results as JSON. The operation lists all tables in the current database, enabling the assistant to provide contextual information about available datasets. Because it is read‑only, developers can confidently integrate the server into production workflows, knowing that no accidental writes or schema changes can occur through the AI interface. This is especially valuable when building analytical assistants, data‑driven conversational agents, or automated reporting tools that need to pull fresh insights from a warehouse on demand.

Key capabilities include:

• Secure, read‑only access to Iceberg tables via Impala, ensuring data integrity while allowing exploration.
• Schema discovery that lets the assistant explain table structures and column types to users or other components.
• Full SQL support for any valid Impala query, enabling complex aggregations, joins, and filtering directly from the LLM.
• Configurable transport (stdio, http, sse) so the server can run locally with Claude Desktop or be exposed as a microservice in cloud deployments.
• Environment‑driven configuration for host, port, credentials, and database selection, simplifying deployment across environments.

Typical use cases span a wide range of real‑world scenarios. A data analyst can ask an AI assistant to “show me the top 10 sales by region” and receive a JSON table instantly, without writing code. A data engineer can let the model prototype queries against staging tables before committing them to production pipelines. In an automated reporting pipeline, a LangChain or OpenAI SDK integration can trigger the server to fetch fresh metrics each time a report is generated, ensuring up‑to‑date insights. Because the server is lightweight and language‑agnostic, it can be dropped into any AI workflow that supports MCP, from single‑user desktops to enterprise‑grade orchestration platforms.

What sets this server apart is its tight coupling with Apache Iceberg and Impala, two industry‑standard technologies for scalable analytics on cloud storage. By leveraging Impala’s performance and Iceberg’s schema evolution features, the MCP server offers a robust, high‑throughput bridge between LLMs and big data lakes. Developers gain the flexibility of an AI assistant that can reason about real, live datasets while maintaining strict read‑only guarantees—a combination that is rare in off‑the‑shelf AI tool integrations.