What is Mcp Iceberg Service

mcp-iceberg-service is a Model Context Protocol (MCP) server that facilitates interaction with Apache Iceberg catalogs. It enables users to discover data lakes and perform metadata searches through a SQL interface integrated with Claude desktop.

Use cases

Use cases for mcp-iceberg-service include querying and managing large datasets in data lakes, performing metadata searches for data governance, and integrating with machine learning workflows to facilitate data access.

How to use

To use mcp-iceberg-service, install it in Claude Desktop by adding specific configuration settings to the ‘claude_desktop_config.json’ file. Ensure you have the necessary prerequisites, including Python 3.10 or higher and access to an Iceberg REST catalog.

Key features

Key features of mcp-iceberg-service include a SQL interface for querying Iceberg tables, integration with PyIceberg for efficient data handling, and support for various SQL operations such as LIST TABLES, DESCRIBE TABLE, SELECT, and INSERT.

Where to use

mcp-iceberg-service is ideal for data lake management, data discovery, and metadata search applications in data engineering, analytics, and machine learning environments.

MCP Iceberg Catalog

A MCP (Model Context Protocol) server implementation for interacting with Apache Iceberg. This server provides a SQL interface for querying and managing Iceberg tables through Claude desktop.

Claude Desktop as your Iceberg Data Lake Catalog

How to Install in Claude Desktop

Installing via Smithery

To install MCP Iceberg Catalog for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @ahodroj/mcp-iceberg-service --client claude

1. Prerequisites

   • Python 3.10 or higher
   • UV package installer (recommended) or pip
   • Access to an Iceberg REST catalog and S3-compatible storage

2. How to install in Claude Desktop
   Add the following configuration to claude_desktop_config.json:

{
  "mcpServers": {
    "iceberg": {
      "command": "uv",
      "args": [
        "--directory",
        "PATH_TO_/mcp-iceberg-service",
        "run",
        "mcp-server-iceberg"
      ],
      "env": {
        "ICEBERG_CATALOG_URI": "http://localhost:8181",
        "ICEBERG_WAREHOUSE": "YOUR ICEBERG WAREHOUSE NAME",
        "S3_ENDPOINT": "OPTIONAL IF USING S3",
        "AWS_ACCESS_KEY_ID": "YOUR S3 ACCESS KEY",
        "AWS_SECRET_ACCESS_KEY": "YOUR S3 SECRET KEY"
      }
    }
  }
}

Design

Architecture

The MCP server is built on three main components:

1. MCP Protocol Handler

   • Implements the Model Context Protocol for communication with Claude
   • Handles request/response cycles through stdio
   • Manages server lifecycle and initialization

2. Query Processor

   • Parses SQL queries using sqlparse
   • Supports operations:
     • LIST TABLES
     • DESCRIBE TABLE
     • SELECT
     • INSERT

3. Iceberg Integration

   • Uses pyiceberg for table operations
   • Integrates with PyArrow for efficient data handling
   • Manages catalog connections and table operations

PyIceberg Integration

The server utilizes PyIceberg in several ways:

1. Catalog Management

   • Connects to REST catalogs
   • Manages table metadata
   • Handles namespace operations

2. Data Operations

   • Converts between PyIceberg and PyArrow types
   • Handles data insertion through PyArrow tables
   • Manages table schemas and field types

3. Query Execution

   • Translates SQL to PyIceberg operations
   • Handles data scanning and filtering
   • Manages result set conversion

Further Implementation Needed

1. Query Operations

   • [ ] Implement UPDATE operations
   • [ ] Add DELETE support
   • [ ] Support for CREATE TABLE with schema definition
   • [ ] Add ALTER TABLE operations
   • [ ] Implement table partitioning support

2. Data Types

   • [ ] Support for complex types (arrays, maps, structs)
   • [ ] Add timestamp with timezone handling
   • [ ] Support for decimal types
   • [ ] Add nested field support

3. Performance Improvements

   • [ ] Implement batch inserts
   • [ ] Add query optimization
   • [ ] Support for parallel scans
   • [ ] Add caching layer for frequently accessed data

4. Security Features

   • [ ] Add authentication mechanisms
   • [ ] Implement role-based access control
   • [ ] Add row-level security
   • [ ] Support for encrypted connections

5. Monitoring and Management

   • [ ] Add metrics collection
   • [ ] Implement query logging
   • [ ] Add performance monitoring
   • [ ] Support for table maintenance operations

6. Error Handling

   • [ ] Improve error messages
   • [ ] Add retry mechanisms for transient failures
   • [ ] Implement transaction support
   • [ ] Add data validation