Elasticsearch 7.x Data Access Gateway (MCP)

This service layer bridges clients to Elasticsearch 7.x environments, ensuring protocol conformity.

Core Capabilities

• Exposes an MCP service layer endpoint for seamless integration with ES 7.x data stores.
• Handles routine cluster health checks and metadata retrieval.
• Enables full spectrum querying, including complex pipeline aggregations, result snippet generation (highlighting), result ordering, and other sophisticated search parameters.
• Simplifies feature consumption for any connected MCP client application.

Prerequisites

• Runtime environment: Python version 3.10 or newer.
• Target Data Store: Elasticsearch cluster running version 7.x (7.17.x recommended).

Deployment Instructions

Automated Installation via Smithery

For effortless setup in environments like Claude Desktop using Smithery:

bash npx -y @smithery/cli install @imlewc/elasticsearch7-mcp-server --client claude

Local Setup

bash pip install -e .

Configuration Variables

The following environment variables are mandatory for operation:

• ELASTIC_HOST: The URI endpoint for the Elasticsearch cluster (e.g., http://data-node:9200).
• ELASTIC_USERNAME: Credentials for cluster authentication.
• ELASTIC_PASSWORD: Secret key for cluster authentication.
• MCP_PORT: (Optional) The network port for the MCP server to listen on; defaults to 9999.

Containerized Orchestration

1. Establish a .env configuration file defining your security credentials:

ELASTIC_PASSWORD=your_highly_secret_password_here

1. Launch the services via Docker Compose:

bash docker-compose up -d

This command initiates a robust, three-member ES 7.17.10 cluster, the associated Kibana instance, and this MCP interface service.

Client Interaction Example

Any standard MCP client library can connect to this gateway:

python from mcp import MCPClient

client = MCPClient("gateway-address:9999") status_check = client.call("es-ping") print(status_check) # Expected output: {"success": true}

Available Remote Procedures (RPCs)

Currently supported methods callable via the MCP client:

• es-ping: Verifies connectivity to the Elasticsearch backend.
• es-info: Fetches metadata regarding the cluster status.
• es-search: Executes document retrieval and analytical queries against a specified index.

Query Examples

Standard Retrieval

python

Execution of a simple keyword matching query

retrieval_results = client.call("es-search", { "index": "product_catalog", "query": { "match": { "description": "premium widgets" } }, "size": 10, "from": 0 })

Analytical Aggregation

python

Requesting only statistical summaries without document payloads

stats_summary = client.call("es-search", { "index": "sales_data", "size": 0,
"aggs": { "product_groups": { "terms": { "field": "department.keyword", "size": 10 } }, "revenue_total": { "sum": { "field": "transaction_value" } } } })

Complex Filtered Search with Presentation Tuning

python

Advanced query combining boolean logic, range filtering, result ordering, and field masking

complex_retrieval = client.call("es-search", { "index": "article_archive", "query": { "bool": { "must": [ {"match": {"body_text": "machine learning concepts"}} ], "filter": [ {"range": {"publish_year": {"gte": 2020}}} ] } }, "sort": [ {"relevance_score": {"order": "desc"}}, "_doc_id" ], "highlight": { "fields": { "body_text": {}, "title": {} } }, "_source": ["article_id", "publish_year", "title"] })

Contribution Guidelines

1. Clone the source repository.
2. Install required development packages.
3. Execute the server executable: elasticsearch7-mcp-server

Governing Agreement

[See LICENSE file for terms of use]

中文文档

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