// may the 4th be with you⚔️
databasesanalytics-data

Apache Iceberg

by ryft-io

Access Apache Iceberg tables in AWS: explore catalogs, schemas, properties and partitions—no queries or code required.

Provides direct access to Apache Iceberg tables stored in AWS, enabling exploration of catalogs, schemas, properties, and partition information without complex queries or code.

github stars

42

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Natural language interfaceAWS Glue integrationNo complex SQL required

best for

  • / Data engineers exploring lakehouse architectures
  • / Analysts investigating Iceberg table structures
  • / Teams working with AWS Glue catalogs

capabilities

  • / Browse Apache Iceberg catalogs and schemas
  • / Inspect table properties and metadata
  • / View partition information
  • / Query table structures using natural language
  • / Access AWS Glue managed catalogs

what it does

Access Apache Iceberg tables stored in AWS through natural language queries in Claude or other MCP clients. Explore catalogs, schemas, and partition information without writing code.

about

Apache Iceberg is a community-built MCP server published by ryft-io that provides AI assistants with tools and capabilities via the Model Context Protocol. Access Apache Iceberg tables in AWS: explore catalogs, schemas, properties and partitions—no queries or code required. It is categorized under databases, analytics data.

how to install

You can install Apache Iceberg in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

Apache-2.0

Apache Iceberg is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Iceberg Logo # IcebergMCP 🚀 AI-native Lakehouse Integration [![PyPI - Version](https://img.shields.io/pypi/v/iceberg-mcp.svg)](https://pypi.org/project/iceberg-mcp) [![License](https://img.shields.io/github/license/ryft-io/iceberg-mcp)](https://github.com/ryft-io/iceberg-mcp/blob/main/LICENSE)
IcebergMCP is a [Model Context Protocol](https://modelcontextprotocol.io/) (MCP) server that lets you interact with your [Apache Iceberg™](https://iceberg.apache.org/) Lakehouse using natural language in Claude, Cursor, or any other MCP client. ## Table of Contents - [Installation](#installation) - [Prerequisites](#prerequisites) - [Claude](#claude) - [Cursor](#cursor) - [Configuration](#configuration) - [Available Tools](#available-tools) - [Examples](#examples) - [Limitations & Security Considerations](#limitations--security-considerations) - [Contributing](#contributing) ## Installation ### Prerequisites - Apache Iceberg™ catalog managed in AWS Glue - AWS profile configured on the machine, with access to the catalog - `uv` package manager - install via `brew install uv` or see [official installation guide](https://docs.astral.sh/uv/getting-started/installation/) ### Claude 1. Inside Claude, go to Settings > Developer > Edit Config > claude_desktop_config.json 2. Add the following: ```json { "mcpServers": { "iceberg-mcp": { "command": "uv", // If uv can't be found, replace with full absolute path to uv "args": [ "run", "--with", "iceberg-mcp", "iceberg-mcp" ], "env": { "ICEBERG_MCP_PROFILE": "" } } } } ``` ### Cursor 1. Inside Cursor, go to Settings -> Cursor Settings -> MCP -> Add new global MCP server 2. Add the following: ```json { "mcpServers": { "iceberg-mcp": { "command": "uv", // If uv can't be found, replace with full absolute path to uv "args": [ "run", "--with", "iceberg-mcp", "iceberg-mcp" ], "env": { "ICEBERG_MCP_PROFILE": "" } } } } ``` ## Configuration Environment variables can be used to configure the AWS connection: - `ICEBERG_MCP_PROFILE` - The AWS profile name to use. This role will be assumed and used to connect to the catalog and the object storage. If not specified, the default role will be used. - `ICEBERG_MCP_REGION` - The AWS region to use. This is used to determine the catalog and object storage location. `us-east-1` by default. ## Available Tools The server provides the following tools for interacting with your Apache Iceberg™ tables: - `get_namespaces`: Gets all namespaces in the Apache Iceberg™ catalog - `get_iceberg_tables`: Gets all tables for a given namespace - `get_table_schema`: Returns the schema for a given table - `get_table_properties`: Returns table properties for a given table, like total size and record count - `get_table_partitions`: Gets all partitions for a given table ## Examples Once installed and configured, you can start interacting with your Apache Iceberg™ tables through your MCP client. Here are some simple examples of how to interact with your lakehouse: 1. "List all namespaces in my catalog" 2. "List all tables for the namespace called `bronze`" 3. "What are all the string columns in the table `raw_events`? 4. "What is the size of the `raw_events` table?" 5. "Generate an SQL query that calculates the sum and the p95 of all number columns in `raw_metrics` for all VIP users from `users_info`" 5. "Why did the queries on `raw_events` recently become much slower?" ## Limitations & Security Considerations - All tools are currently read-only and cannot modify or delete data from your lakehouse - Currently supported catalogs: - AWS Glue - Apache Iceberg™ REST Catalog (coming soon!) ## Contributing Contributions are welcome! Please feel free to submit a Pull Request.

FAQ

What is the Apache Iceberg MCP server?
Apache Iceberg is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Apache Iceberg?
This profile displays 29 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.

Discussion

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Ratings

4.829 reviews
  • Dhruvi Jain· Dec 28, 2024

    Apache Iceberg is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Oshnikdeep· Nov 19, 2024

    Apache Iceberg is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Ganesh Mohane· Oct 10, 2024

    We evaluated Apache Iceberg against two servers with overlapping tools; this profile had the clearer scope statement.

  • Yusuf Kapoor· Sep 9, 2024

    Apache Iceberg is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Layla Brown· Sep 5, 2024

    I recommend Apache Iceberg for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Yusuf Sharma· Aug 28, 2024

    We wired Apache Iceberg into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Layla Tandon· Aug 24, 2024

    Strong directory entry: Apache Iceberg surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Layla Taylor· Jul 19, 2024

    We evaluated Apache Iceberg against two servers with overlapping tools; this profile had the clearer scope statement.

  • Li Harris· Jul 15, 2024

    Apache Iceberg has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Rahul Santra· Jul 3, 2024

    Useful MCP listing: Apache Iceberg is the kind of server we cite when onboarding engineers to host + tool permissions.

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