Elasticsearch MCP Server

What is elasticsearch-mcp-server

elasticsearch-mcp-server is a Model Context Protocol (MCP) server developed by cr7258 that facilitates interaction with Elasticsearch. This server provides a robust interface for managing and querying Elasticsearch clusters, enabling users to search documents, analyze indices, and manage cluster operations efficiently. As part of the MCP ecosystem, it allows AI systems to seamlessly connect with Elasticsearch, providing a universal standard for accessing and managing data securely.

How to Use elasticsearch-mcp-server

To start using the elasticsearch-mcp-server, you need to set up an Elasticsearch cluster. This can be easily done using Docker Compose, which will initiate a three-node cluster along with Kibana for visualization. The default credentials are set for ease of access, with the username elastic and password test123.

Once the cluster is running, you can integrate elasticsearch-mcp-server with AI tools such as Claude Desktop. There are several ways to do this:

• Option 1: Installing via Smithery: Use Smithery CLI to install the server, which simplifies the process by handling the installation automatically.

• Option 2: Using uvx: This method involves configuring Claude Desktop to use uvx, which installs the server package directly from PyPI.

• Option 3: Using uv with local development: This approach requires cloning the server's repository and configuring the local path in Claude Desktop's settings.

After configuration, you can use natural language commands within Claude to interact with your Elasticsearch cluster, such as listing all indices or checking cluster health.

Key Features of elasticsearch-mcp-server

Index Operations:

• list_indices: Enumerates all indices present in the Elasticsearch cluster, providing an overview of the data structure.
• get_mapping: Retrieves the mapping configuration for a specified index, which is crucial for understanding the data schema and structure.
• get_settings: Fetches the settings configuration for a specific index, allowing you to understand and modify index parameters.

Document Operations:

• search_documents: Executes searches within an index using Elasticsearch Query DSL, enabling complex and precise data retrieval.

Cluster Operations:

• get_cluster_health: Provides the current health status of the cluster, helping you monitor and maintain the cluster's performance.
• get_cluster_stats: Offers detailed statistical information about the cluster, useful for analyzing performance and usage metrics.

These features make elasticsearch-mcp-server a powerful tool for managing Elasticsearch clusters, providing essential capabilities that integrate seamlessly into broader AI workflows. Whether you're handling large datasets or managing intricate queries, elasticsearch-mcp-server offers the flexibility and functionality needed to enhance your data management processes.