Enables querying, managing, and analyzing Elasticsearch data through natural language interactions, providing tools to search indices, list available indices, and retrieve index mappings.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Elastic MCP search the logs index for recent error messages"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
https://github.com/user-attachments/assets/907c3f6f-807c-4805-879a-649c74804c29
Connect to your Elasticsearch cluster from any MCP-compatible client (such as Claude Desktop) using the Model Context Protocol (MCP). This server exposes your Elasticsearch data and operations via the MCP interface, enabling agents and applications to query, manage, and analyze your data through natural language interactions.
Python 3.8+
Elasticsearch running and accessible
uv or pip for dependency management
Using uv package manager:
uv pip install -r requirements.txtRelated MCP server: Elasticsearch MCP Server
Test using MCP Inspector
ELASTIC_URL="http://localhost:9200" ELASTIC_USERNAME="your_username" ELASTIC_PASSWORD="your_password" fastmcp dev tools/elastic_tool.pyor
Run the mcp server by
ELASTIC_URL="http://localhost:9200" ELASTIC_USERNAME="elastic" ELASTIC_PASSWORD="hKsXqDsd" python3 tools/elastic_tool.pyand run mcp client in another terminal by
python3 mcp_client.pyor
Add to the Claude Desktop by editing the claude_desktop_config.json and add the following code snippet
{
"mcpServers": {
"Elastic MCP Server": {
"command": "uv",
"args": [
"run",
"--with-requirements",
"<absolute path to requirements.txt>",
"fastmcp",
"run",
"<absolute path to elastic_tool.py>"
],
"env": {
"ELASTIC_URL": "http://localhost:9200",
"ELASTIC_USERNAME": "your_username",
"ELASTIC_PASSWORD": "your_password"
}
}
}
}
search_index: Search an index with a query string.
list_indices: List all indices (excluding system indices).
get_index_mappings: Get mappings for a specific index.
MIT License
For production, do not hardcode credentials.
For more info on MCP, see FastMCP documentation.
Get the mappings of a specific Elasticsearch index.
Inputs: index (required).
List all indices in the Elasticsearch cluster.
Inputs: no named parameters.
Search an Elasticsearch index with a simple query string.
Inputs: index (required), query (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 3 tools
Each tool has a clearly distinct purpose: get_index_mappings retrieves schema information for a specific index, list_indices enumerates all indices, and search_index performs queries on an index. There is no overlap in functionality, making it easy for an agent to select the correct tool.
All tool names follow a consistent verb_noun pattern (get_index_mappings, list_indices, search_index) with clear, descriptive verbs and nouns. This uniformity enhances readability and predictability.
With only 3 tools, the server feels thin for an Elasticsearch domain, which typically involves more operations like creating/deleting indices, updating mappings, or complex queries. While the tools cover basic inspection and search, the scope is limited.
The tool set is severely incomplete for Elasticsearch operations. It lacks essential CRUD actions such as creating or deleting indices, updating mappings, and handling documents (e.g., index, update, delete). This will cause agent failures when trying to perform common tasks beyond listing and searching.
Search your AI chat history (ChatGPT, Claude, Codex) from any MCP client. Remote, private, read-only
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