Elasticsearch MCP Server

Name: Elasticsearch MCP Server
Author: sunilemanjee

@sunilemanjee

リポジトリ

Elasticsearch MCP Server について

概要はまだありません

基本情報

カテゴリ

データベース

ランタイム

python

トランスポート

stdio

公開者

sunilemanjee

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "Elastic-Python-MCP-Server": {
      "command": "python3",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Elasticsearch MCP Server?

Elasticsearch MCP Server is a Python‑based MCP (Model Context Protocol) server that provides an interface for searching and analyzing property data using Elasticsearch. It was converted from a Jupyter notebook and connects to Elasticsearch Serverless, using ELSER for semantic search and Google Maps API for geocoding.

How to use Elasticsearch MCP Server?

Clone the repository and copy env_config.template.sh to env_config.sh.
Set environment variables (ES_URL, ES_API_KEY, GOOGLE_MAPS_API_KEY, etc.) in env_config.sh.
Create a Python virtual environment, install dependencies from requirements.txt, and source the config file.
Run ./run_server.sh; the server starts on port 8001 by default.

Key features of Elasticsearch MCP Server

Property search by location, price, bedrooms, bathrooms, square footage, features, tax, and maintenance.
Geocoding integration using Google Maps API.
Connects to Elasticsearch Serverless with ELSER semantic search.
Supports custom search templates.
Provides three MCP tools: get_properties_template_params, geocode_location, search_template.

Use cases of Elasticsearch MCP Server

Searching a property database with natural language queries.
Converting location strings into geographic coordinates for proximity searches.
Enabling LLMs to retrieve structured property data via MCP.
Real‑estate analysis and portfolio management.

FAQ from Elasticsearch MCP Server

What are the prerequisites?

Python 3.x, an Elasticsearch Serverless instance, a Google Maps API key (with Geocoding and Maps JavaScript APIs enabled), and the required Python packages.

How do I configure environment variables?

Copy env_config.template.sh to env_config.sh and set ES_URL, ES_API_KEY, GOOGLE_MAPS_API_KEY, and optionally PROPERTIES_SEARCH_TEMPLATE, ELSER_INFERENCE_ID, ES_INDEX, and MCP_PORT.

How do I run the server?

Source the environment variables (source env_config.sh) and execute ./run_server.sh. Verify with curl -v http://localhost:8001/sse.

Can I restrict the Elasticsearch API key to read‑only?

Yes. After ingestion, create or edit an API key with privileges limited to monitor cluster and read/view_index_metadata on properties and properties_raw indices.

What MCP endpoints are available?

Three tools: get_properties_template_params (returns search template parameters), geocode_location (converts location to coordinates), and search_template (performs property searches).

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