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Druid MCP Server

@christian-schlichtherle

代码仓库

基本信息

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作者

christian-schlichtherle

提交者

Christian Schlichtherle

关于 Druid MCP Server

An MCP server for comprehensive read-only access to one or more Apache Druid clusters for ad-hoc data analysis, monitoring, troubleshooting, and comparison.

#druid#apache-druid#mcp#mcp-server

Druid MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "druid": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/druid-mcp",
        "run",
        "main.py"
      ],
      "env": {
        "DRUID_CLUSTERS": "dev=http://localhost:8088 stage=http://localhost:8089 prod=http://localhost:8090"
      }
    }
  }
}

Druid MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Druid MCP Server?

Druid MCP Server provides an MCP server for read-only access to one or more Apache Druid clusters. It enables AI applications to interact with Druid through the Model Context Protocol (MCP), allowing ad-hoc data analysis and comparison across multiple Druid clusters.

How to use Druid MCP Server?

Install dependencies with uv install or pip install -e ., then configure clusters via the DRUID_CLUSTERS environment variable (whitespace-separated key=value pairs). Run the server in development with mcp dev main.py or production with mcp run main.py. Integrate with Claude Desktop using mcp install main.py. All tools (except cluster management) require an explicit cluster parameter as the first argument.

Key features of Druid MCP Server

  • Multi-cluster support: connect to and query multiple clusters simultaneously
  • Query execution: SQL and native JSON queries
  • Datasource management: list, explore, and inspect schemas
  • Ingestion monitoring: supervisor and task status tracking
  • Cluster operations: service health monitoring and segment analysis
  • Lookup management: query and inspect lookup tables
  • Smart analysis prompts: pre-built prompts for common analysis tasks
  • Schema caching: 5-minute TTL for resource endpoints

Use cases of Druid MCP Server

  • Compare datasource schemas and data distribution across development, staging, and production clusters
  • Execute ad-hoc SQL or native queries on any configured Druid cluster
  • Monitor ingestion health and task status across multiple environments simultaneously
  • Perform cross-cluster data quality checks and time-period comparisons
  • Explore datasource structure and content with AI-assisted prompts

常见问题

1

What access mode does Druid MCP Server use?

Druid MCP Server provides read-only access to your Druid clusters. No write operations are supported.

2

What are the prerequisites for running Druid MCP Server?

Python 3.11+, access to an Apache Druid cluster, and an MCP-compatible client (e.g., Claude Desktop).

3

How does multi-cluster support work?

Configure multiple Druid clusters with the `DRUID_CLUSTERS` environment variable using `name=url` pairs. Each tool accepts an explicit cluster name parameter, enabling simultaneous queries to different clusters without hidden state.

4

Does Druid MCP Server support native Druid queries?

Yes, it supports both SQL queries via `execute_sql_query` and native JSON queries (timeseries, topN, groupBy, etc.) via `execute_native_query`.

5

Is there any caching for Druid API responses?

Schema caching is implemented with a 5-minute TTL for resource endpoints only, improving performance for repeated schema accesses.

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