Which integrations are available for this server?

Provides tools to manage and query a knowledge base within Elastic, including document ingestion, text chunking, and semantic search retrieval. Allows indexing documentation, extracting text from PDFs, and performing semantic search queries against an Elasticsearch knowledge base.

How do I use ElasticMind-MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@ElasticMind-MCP search the documentation for our company's security protocols" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

SME Knowledge Base — MCP Server

This repository provides an MCP (Model Context Protocol) server that indexes documentation into Elasticsearch and exposes tools to query it from MCP-compatible clients such as Claude Desktop, Cursor, and GitHub Copilot.

📌 Features

Smart Indexing: Uses deterministic IDs to prevent duplicate entries in Elasticsearch.
Semantic Search: Query the knowledge base using Elasticsearch's matching capabilities.
Dynamic Updates: Add new text content directly via MCP tools.
Robustness: Gracefully handles database connection failures.

Prerequisites

Before running the server, ensure the following are installed:

Python 3.11+
Docker (for running Elasticsearch)
uv (Python package and project manager)

📦 Setup

1. Start Elasticsearch

The server requires a running Elasticsearch instance. You can start one easily using Docker:

docker run -d --name elasticsearch \ -p 9200:9200 -p 9300:9300 \ -e "discovery.type=single-node" \ -e "xpack.security.enabled=false" \ -e ES_JAVA_OPTS="-Xms1g -Xmx1g" \ docker.elastic.co/elasticsearch/elasticsearch:9.1.5

(Note: Ensure the version tag matches your requirements. Version 8.11.0 is used here as a stable default.)

2. Install Dependencies

Navigate to the project directory and install the required Python packages:

uv sync # OR pip install -e .

3. Ingest Data

Place your PDF documents in the input/ folder and run the extraction script to generate the data/docs.json index file:

uv run extraction.py

🧩 Configuration

To use this server with Claude Desktop, Cursor, or GitHub Copilot, you need to configure the MCP settings.

1. Locate Paths

You will need the absolute paths for both the uv executable and your cloned repository.

Find
which uv
Find Repository path:
pwd

2. Edit Configuration File

A template configuration file is provided in mcp.json. You can copy its content, but remember to update the paths to be absolute.

Open Claude Desktop.
Go to Settings > Developer > Edit Config.
Add the following configuration to the mcpServers object in the JSON file:

{ "mcpServers": { "sme-knowledge-base": { "command": "/absolute/path/to/uv", "args": [ "run", "--directory", "/absolute/path/to/my_server_sme", "elastic_server.py" ], "env": { "ES_HOST": "http://localhost:9200" } } } }

Replace

🔧 Available Tools

The server exposes the following tools to the LLM:

Tool Name	Description
`ingest_pdfs`	Scans the `input/` directory for new PDFs, extracts text, updates `docs.json`, and indexes everything into Elasticsearch. Call this after adding new files.
`index_documents`	Manually triggers the indexing process from `data/docs.json` to Elasticsearch. Useful if you've modified the JSON file directly.
`add_text_to_index`	Adds a new text document to the knowledge base. Features: • Updates both persistent storage (`docs.json`) and Elasticsearch. • Automatically chunks content > 1000 words. • Generates unique IDs.
`query_knowledge_base`	Accepts a search query string and returns the top 2 most relevant document sections (Heading + Content).

📖 Example Workflow

Start Elasticsearch: Ensure your Docker container is running.
docker start elasticsearch
Add Documents: Drop any PDF files you want to index into the input/ folder.
Start Server: When you open Claude Desktop or Cursor, the server starts automatically.
- It will scan input/, extract text from new PDFs, and index them into Elasticsearch.
Interact:
- "What does the document say about [topic]?" (Uses query_knowledge_base)
- "Add this meeting note to the knowledge base: [content]" (Uses add_text_to_index)

Troubleshooting

Connection Refused: Ensure the Docker container is running (docker ps) and port 9200 is accessible.
Path Errors: Double-check that the paths in your config JSON are absolute (start with /) and point to the correct locations.