Apache Iceberg logo

Apache Iceberg

Organization
ryft-io

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

Publisherryft-io
Repositoryiceberg-mcp
LanguagePython
Forks
5
Stars
42
Available tools
0
Transport typestdio
Categories
LicenseApache-2.0
Links
  • Connect tools to AI workflows

    Apache Iceberg exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    42 stars and 5 forks from the linked repository.

IcebergMCP 🚀

AI-native Lakehouse Integration

PyPI - Version License

IcebergMCP is a Model Context Protocol (MCP) server that lets you interact with your Apache Iceberg™ Lakehouse using natural language in Claude, Cursor, or any other MCP client.

Table of Contents

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

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": "<aws-profile-name>"
      }
    }
  }
}

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": "<aws-profile-name>"
      }
    }
  }
}

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"
  6. "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.

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "iceberg-mcp": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "iceberg-mcp",
        "iceberg-mcp"
      ],
      "env": {
        "ICEBERG_MCP_PROFILE": "<aws-profile-name>"
      }
    }
  }
}

Use Apache Iceberg MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Apache Iceberg is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Apache Iceberg as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Apache Iceberg MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "apache-iceberg": {
      "command": "npx",
      "args": [
        "-y",
        "iceberg-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Apache Iceberg MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Apache Iceberg MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Apache Iceberg to help me with this task?
Apache Iceberg
Sure. I read it.
Here is what I found using Apache Iceberg.

Frequently asked questions

What is the Apache Iceberg MCP server used for?

Apache Iceberg is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Apache Iceberg MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Apache Iceberg with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Apache Iceberg MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Apache Iceberg connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Apache Iceberg MCP to TypingMind?

Apache Iceberg runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Apache Iceberg MCP provide in TypingMind?

Apache Iceberg exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Apache Iceberg MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Apache Iceberg requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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