IcebergMCP
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# IcebergMCP 🚀
<strong>AI-native Lakehouse Integration</strong>
[](https://pypi.org/project/iceberg-mcp)
[](https://github.com/ryft-io/iceberg-mcp/blob/main/LICENSE)
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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.
<video src="https://github.com/user-attachments/assets/907180f3-27ad-401a-9fa0-f3178cd290de"></video>
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## 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": "<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`"
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.
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose targeting different aspects of Iceberg metadata: listing tables, listing namespaces, retrieving partitions, properties, and schema. There is no overlap in functionality, making tool selection straightforward for an agent.
All tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive suffixes (e.g., get_iceberg_tables, get_namespaces, get_table_partitions). The naming is uniform and predictable across all five tools.
With 5 tools, this server is well-scoped for its purpose of retrieving Iceberg catalog metadata. Each tool serves a specific, non-trivial function, and the count is neither too sparse nor overwhelming for the domain.
The toolset covers read operations for Iceberg metadata (tables, namespaces, partitions, properties, schema) but lacks any write, update, or delete capabilities. While it provides a solid foundation for querying, it is incomplete for full lifecycle management of Iceberg resources.