Elasticsearch MCP Server

@sunilemanjee

リポジトリ

基本情報

カテゴリ

データベース

トランスポート

stdio

作者

sunilemanjee

Elasticsearch MCP Server について

概要はまだありません

Elasticsearch MCP Server の設定

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"
      ]
    }
  }
}

Elasticsearch MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

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.

よくある質問

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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