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.
@sunilemanjee
基本信息
暂无概览
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"Elastic-Python-MCP-Server": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
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.
env_config.template.sh to env_config.sh.ES_URL, ES_API_KEY, GOOGLE_MAPS_API_KEY, etc.) in env_config.sh.requirements.txt, and source the config file../run_server.sh; the server starts on port 8001 by default.get_properties_template_params, geocode_location, search_template.Python 3.x, an Elasticsearch Serverless instance, a Google Maps API key (with Geocoding and Maps JavaScript APIs enabled), and the required Python packages.
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`.
Source the environment variables (`source env_config.sh`) and execute `./run_server.sh`. Verify with `curl -v http://localhost:8001/sse`.
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.
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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