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Elasticsearch MCP Server

@sunilemanjee

Repository

Basic information

Category

Databases

Transports

stdio

Author

sunilemanjee

About Elasticsearch MCP Server

No overview available yet

Elasticsearch MCP Server config

Add this server to your MCP-compatible client using the configuration below.

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

Elasticsearch MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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?

  1. Clone the repository and copy env_config.template.sh to env_config.sh.
  2. Set environment variables (ES_URL, ES_API_KEY, GOOGLE_MAPS_API_KEY, etc.) in env_config.sh.
  3. Create a Python virtual environment, install dependencies from requirements.txt, and source the config file.
  4. 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.

Frequently asked questions

1

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.

2

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`.

3

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`.

4

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.

5

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).

Comments

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