I craft unique cereal names, stories, and ridiculously cute Cereal Baby images.

Elasticsearch7-MCP-Server
Elasticsearch7 MCP Server
3 years
Works with Finder
1
Github Watches
3
Github Forks
1
Github Stars
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]
相关推荐
Evaluator for marketplace product descriptions, checks for relevancy and keyword stuffing.
Confidential guide on numerology and astrology, based of GG33 Public information
A geek-themed horoscope generator blending Bitcoin prices, tech jargon, and astrological whimsy.
Converts Figma frames into front-end code for various mobile frameworks.
Advanced software engineer GPT that excels through nailing the basics.
Therapist adept at identifying core issues and offering practical advice with images.
Entdecken Sie die umfassendste und aktuellste Sammlung von MCP-Servern auf dem Markt. Dieses Repository dient als zentraler Hub und bietet einen umfangreichen Katalog von Open-Source- und Proprietary MCP-Servern mit Funktionen, Dokumentationslinks und Mitwirkenden.
Ein einheitliches API-Gateway zur Integration mehrerer Ethercan-ähnlicher Blockchain-Explorer-APIs mit Modellkontextprotokoll (MCP) für AI-Assistenten.
Mirror ofhttps: //github.com/bitrefill/bitrefill-mcp-server
Reviews

user_4wKmZEii
I've been using the elasticsearch7-mcp-server from imlewc and it's been a game-changer for my applications. The seamless integration and robust performance make data management a breeze. The user-friendly interface and comprehensive documentation on GitHub are incredibly helpful. Highly recommend for anyone looking to optimize their elasticsearch usage.