Is this MCP server read-only?
Yes, this project builds a read-only MCP server. For full read, write, update, delete, and action capabilities, CData offers a separate free CData MCP Server for Elasticsearch (beta).
@CDataSoftware
基本信息
This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free [CData MCP Server for Elasticsearch (beta)](https://www.cdata.com/download/download.aspx?sku=EEZK-V&type=beta).
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"{classname_dash}": {
"command": "PATH\\TO\\java.exe",
"args": [
"-jar",
"PATH\\TO\\CDataMCP-jar-with-dependencies.jar",
"PATH\\TO\\elasticsearch.prp"
]
}
}
}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。
Elasticsearch MCP Server by CData is a read-only Model Context Protocol (MCP) server that allows LLMs such as Claude Desktop to query live Elasticsearch data using natural language. It wraps the CData JDBC Driver for Elasticsearch, which exposes Elasticsearch as relational SQL models, and makes that data available through a simple MCP interface — no SQL required.
Clone the repository, build the JAR with Maven (mvn clean install), download and license the CData JDBC Driver for Elasticsearch, configure a connection string using the driver's utility, and create a .prp file with JDBC properties. Then add the server entry to your Claude Desktop config file (claude_desktop_config.json) pointing to Java and the JAR, and restart Claude Desktop. The server runs via stdio.
get_tables, get_columns, and run_query.prp) fileYes, this project builds a read-only MCP server. For full read, write, update, delete, and action capabilities, CData offers a separate free CData MCP Server for Elasticsearch (beta).
You need Java (to run the JAR file), the CData JDBC Driver for Elasticsearch (installed and licensed), and a client that supports MCP over `stdio` (like Claude Desktop).
It uses a JDBC connection string configured in a `.prp` file. The driver models Elasticsearch data as relational tables and exposes them via MCP tools.
The server uses `stdio` transport, so it can only be used with clients that run on the same machine as the server.
Data remains in the live Elasticsearch instance; the server retrieves it on demand. No data is stored by the MCP server itself.
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