Skip to main content
Glama
Sign In
enesjakozh

MCP Iceberg Catalog

by ahodroj
GitHub
Python
2
OverviewInspectNewSchemaRelated ServersReviewsScore
Need Help?View Source CodeReport Issue

Integrations

  • Provides a SQL interface for querying and managing Apache Iceberg tables, allowing users to list tables, describe table structures, execute SELECT queries, and insert data into Iceberg data lakes.

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

You must be authenticated.

A
security – no known vulnerabilities
F
license - not found
A
quality - confirmed to work

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

A Model Context Protocol server that provides a SQL interface for querying and managing Apache Iceberg tables through Claude desktop, allowing natural language interaction with Iceberg data lakes.

  1. Claude Desktop as your Iceberg Data Lake Catalog
    1. How to Install in Claude Desktop
      1. Installing via Smithery
    2. Design
      1. Architecture
      2. PyIceberg Integration
    3. Further Implementation Needed

      Related MCP Servers

      • MCP Snowflake Service

        datawiz168
        -
        security
        A
        license
        -
        quality
        A Model Context Protocol server that enables Claude to execute SQL queries on Snowflake databases with automatic connection lifecycle management.
        Last updated -
        28
        Python
        MIT License
        • Apple
        • Linux

      • Astra DB MCP Serverofficial

        datastax
        A
        security
        A
        license
        A
        quality
        A Model Context Protocol server that allows Large Language Models to interact with Astra DB databases, providing tools for managing collections and records through natural language commands.
        Last updated -
        10
        115
        12
        TypeScript
        Apache 2.0
        • Apple

      • MCP Trino Server

        alaturqua
        -
        security
        A
        license
        -
        quality
        A Model Context Protocol server that provides seamless integration with Trino and Iceberg, enabling data exploration, querying, and table maintenance through a standard interface.
        Last updated -
        14
        Python
        Apache 2.0

      • MCP SQL Server

        JubinSaniei
        -
        security
        -
        license
        -
        quality
        A Model Context Protocol server that allows Large Language Models like Claude to execute SQL queries, explore database schemas, and maintain persistent connections to SQL Server databases.
        Last updated -
        TypeScript

      View all related MCP servers

      Appeared in Searches

      New MCP Servers

      ID: qy7z935pqj