Iceberg MCP Server

Iceberg MCP is a lightweight, asynchronous Model Context Protocol server designed to bridge AI assistants with Apache Iceberg data catalogs. It resolves the common pain point of querying large, partitioned datasets stored in Iceberg by exposing a set of high‑level tools that translate catalog metadata into structured responses. Developers can therefore embed data discovery, schema inspection, and property retrieval directly into conversational agents without writing custom connectors or handling raw Iceberg APIs.

The server supports two primary catalog backends: Rest Catalogs and AWS Glue, both of which are widely used in cloud data lakes. By configuring environment variables, the MCP automatically adapts to the chosen backend, handling authentication and endpoint resolution internally. Once connected, the server offers four intuitive tools: , , , and . These commands let an assistant list available namespaces, enumerate tables within a namespace, fetch the detailed schema of a table, and retrieve arbitrary table properties—all with a single API call. This abstraction removes the need for developers to write repetitive code around Iceberg’s REST endpoints or Glue SDKs, enabling rapid prototyping of data‑aware AI workflows.

In practical scenarios, Iceberg MCP shines in data exploration and governance. A data analyst can ask a conversational AI to list all tables under a business unit namespace, and the assistant will return a structured table of results. A data engineer might request the schema of a newly created partitioned table to verify column types before downstream processing. Because the server logs every request and response, teams can audit interactions for compliance or troubleshoot performance issues without exposing raw catalog traffic. The asynchronous design ensures that long‑running metadata queries do not block the assistant’s UI, maintaining a responsive user experience.

Integration is straightforward: after installing the binary, developers add the MCP server to their Claude Desktop configuration via a JSON snippet that specifies command path and environment variables. Once registered, the assistant automatically discovers the new tools during session initialization, making them available as native actions. The same configuration pattern can be reused for other MCP servers, keeping the workflow consistent across different data sources.

Unique to Iceberg MCP is its focus on catalog‑level logging and asynchronous execution, which are not common in other MCP implementations. By capturing detailed logs, developers gain visibility into catalog health and query patterns, while the non‑blocking execution model keeps conversational agents fluid even when interacting with large, complex Iceberg datasets. This combination of ease of use, robust logging, and efficient metadata handling makes Iceberg MCP an essential component for any AI‑driven data platform that relies on Apache Iceberg.