Cloudera Iceberg MCP Server (via Impala)

The Cloudera Iceberg MCP Server bridges large language models with the rich analytical power of Apache Iceberg, accessed through Impala. By exposing a read‑only API, it lets LLMs inspect table schemas and run ad‑hoc queries against production data lakes without risking accidental writes or schema changes. This capability is especially valuable for data scientists and developers who need to prototype insights, generate documentation, or embed analytics into conversational agents while maintaining strict data governance.

At its core, the server offers two simple yet powerful endpoints. The call enumerates all tables in the current Impala database, providing a programmatic view of available datasets. The endpoint accepts any SQL statement and returns the result set serialized as JSON. Because the connection is read‑only, developers can safely integrate it into automated pipelines or interactive notebooks, confident that no user input will alter the underlying data. The server also respects Impala’s authentication mechanisms, allowing fine‑grained access control through user credentials supplied via environment variables.

Key features that set this MCP apart include:

• Zero‑write safety: All interactions are read‑only, eliminating the risk of accidental data modification.
• Schema introspection: Quick discovery of table structures enables dynamic prompt generation and automated documentation.
• JSON‑friendly output: Results are returned in a format that is immediately consumable by downstream AI components, such as LangChain or OpenAI SDKs.
• Transport flexibility: The server can communicate over standard I/O, HTTP, or Server‑Sent Events, making it adaptable to local desktop tools, microservices, or web deployments.

Typical use cases span a range of real‑world scenarios. A data analyst might ask an LLM to “show me the top 10 customers by revenue” and receive a JSON payload that can be rendered in a dashboard. A developer building an AI‑powered chatbot could use to automatically populate context menus with available datasets, enabling users to query data through natural language. In a CI/CD pipeline, the server can validate that new Iceberg tables conform to expected schemas before merging code changes.

Integrating the Cloudera Iceberg MCP into AI workflows is straightforward: developers simply configure their client (Claude Desktop, LangChain, etc.) to point at the server’s endpoint and invoke the two provided methods. Because the server runs over standard input/output or HTTP, it can be launched locally during development or exposed as a microservice in production. Its read‑only nature, combined with Impala’s high‑performance query engine, gives developers a reliable and secure gateway to lakehouse data from within conversational AI agents.