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imlewc_elasticsearch7-mcp-server
Mirror ofhttps://github.com/imlewc/elasticsearch7-mcp-server
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Elasticsearch 7.x MCP Server
An MCP server for Elasticsearch 7.x, providing compatibility with Elasticsearch 7.x versions.
Features
- Provides an MCP protocol interface for interacting with Elasticsearch 7.x
- Supports basic Elasticsearch operations (ping, info, etc.)
- Supports complete search functionality, including aggregation queries, highlighting, sorting, and other advanced features
- Easily access Elasticsearch functionality through any MCP client
Requirements
- Python 3.10+
- Elasticsearch 7.x (7.17.x recommended)
Installation
Installing via Smithery
To install Elasticsearch 7.x MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @imlewc/elasticsearch7-mcp-server --client claude
Manual Installation
pip install -e .
Environment Variables
The server requires the following environment variables:
-
ELASTIC_HOST
: Elasticsearch host address (e.g., http://localhost:9200) -
ELASTIC_USERNAME
: Elasticsearch username -
ELASTIC_PASSWORD
: Elasticsearch password -
MCP_PORT
: (Optional) MCP server listening port, default 9999
Using Docker Compose
- Create a
.env
file and setELASTIC_PASSWORD
:
ELASTIC_PASSWORD=your_secure_password
- Start the services:
docker-compose up -d
This will start a three-node Elasticsearch 7.17.10 cluster, Kibana, and the MCP server.
Using an MCP Client
You can use any MCP client to connect to the MCP server:
from mcp import MCPClient
client = MCPClient("localhost:9999")
response = client.call("es-ping")
print(response) # {"success": true}
API Documentation
Currently supported MCP methods:
-
es-ping
: Check Elasticsearch connection -
es-info
: Get Elasticsearch cluster information -
es-search
: Search documents in Elasticsearch index
Search API Examples
Basic Search
# Basic search
search_response = client.call("es-search", {
"index": "my_index",
"query": {
"match": {
"title": "search keywords"
}
},
"size": 10,
"from": 0
})
Aggregation Query
# Aggregation query
agg_response = client.call("es-search", {
"index": "my_index",
"size": 0, # Only need aggregation results, no documents
"aggs": {
"categories": {
"terms": {
"field": "category.keyword",
"size": 10
}
},
"avg_price": {
"avg": {
"field": "price"
}
}
}
})
Advanced Search
# Advanced search with highlighting, sorting, and filtering
advanced_response = client.call("es-search", {
"index": "my_index",
"query": {
"bool": {
"must": [
{"match": {"content": "search term"}}
],
"filter": [
{"range": {"price": {"gte": 100, "lte": 200}}}
]
}
},
"sort": [
{"date": {"order": "desc"}},
"_score"
],
"highlight": {
"fields": {
"content": {}
}
},
"_source": ["title", "date", "price"]
})
Development
- Clone the repository
- Install development dependencies
- Run the server:
elasticsearch7-mcp-server
License
[License in LICENSE file]
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Reviews

user_TcnxJg26
As a devoted MCP app user, the jira-mcp-server created by aniketkadukar1 has significantly streamlined our project management process. The seamless integration, user-friendly interface, and robust performance make it an indispensable tool for any team. Highly recommended for anyone looking to improve their efficiency and productivity!