NRTSearch MCP Server

NRTSearch MCP Server

Enables AI assistants to search and query Lucene/NRTSearch indexes using natural language, supporting full Lucene query syntax including Boolean, phrase, range, wildcard, and fuzzy searches with highlighting and custom field retrieval.

Category
Visit Server

README

NRTSearch MCP Server

Production-ready Model Context Protocol (MCP) server for Lucene/NRTSearch, with first-class support for AI assistants like GitHub Copilot and Claude.


Features

  • Exposes NRTSearch/Lucene search as a robust MCP server for AI tools
  • Accepts any Lucene query (Boolean, phrase, range, wildcard, fuzzy, etc.)
  • Structured logging, retries, and highlight support
  • Pure unit-testable search logic with full test coverage
  • Easy integration with GitHub Copilot, Claude Desktop, and other MCP clients
  • Modern Python packaging and configuration (Pydantic, pyproject.toml)

Quick Start

git clone https://github.com/tvergilio/nrtsearch-mcp-server.git
cd nrtsearch-mcp-server
./quickstart.sh

This will:

  • Install all dependencies (including MCP SDK)
  • Start the server on the configured port

Usage

CLI / Manual

After installation, you can start the server with either:


# Using the Python module
python -m nrtsearch_mcp.server

# Or, if installed via pip/pipx, use the CLI entrypoint:
nrtsearch-mcp

With GitHub Copilot (VS Code)

  1. Install VS Code and GitHub Copilot
  2. Add nrtsearch-mcp as a Model Context Provider in VS Code settings (see .vscode/settings.json)
  3. Start the server (./quickstart.sh or nrtsearch-mcp)
  4. Use Copilot Chat to query your Lucene indexes in natural language

Configuration

The server is configured via environment variables and/or a JSON config file. By default, it looks for:

  • NRTSEARCH_MCP_CONFIG env var (path to config)
  • ./config.json in the current directory
  • ~/nrtsearch-mcp-config.json in your home directory

Example config:

{
  "nrtsearch_connection": {
    "host": "localhost",
    "port": 8000,
    "use_https": false
  },
  "log_level": "INFO"
}

Key environment variables:

  • LOG_LEVEL (default: INFO)
  • NRTSEARCH_MCP_CONFIG (optional config path)

API: Search Tool

The main tool is nrtsearch/search:

Parameters:

  • index (str): Index name (e.g. yelp_reviews_staging)
  • queryText (str): Full Lucene query (e.g. text:(irish AND pub AND (texas OR tx)))
  • topHits (int, default 10): Number of results (1-100)
  • retrieveFields (list, optional): Fields to return (default: ["text", "stars"])
  • highlight (bool, optional): Highlight matches

Returns:

  • List of hits: {score, stars, text}

Lucene Query Examples:

  • text:(irish AND pub AND (texas OR tx))
  • text:"great coffee"
  • stars:[4 TO 5] AND text:(vegan AND brunch)

Testing

Run all tests (unit, no server needed):

pytest -v

Tests cover:

  • Success, empty, and multiple hits
  • Error handling (HTTP, network, malformed, missing fields)
  • Retry logic
  • Highlight and custom fields
  • Input validation

Project Structure

nrtsearch-mcp-server/
├── nrtsearch_mcp/
│   ├── server.py         # Main MCP server and search logic
│   ├── settings.py       # Pydantic config
│   └── ...
├── tests/               # Unit tests 
├── quickstart.sh        # One-step install & run
├── requirements.txt     # Python dependencies
├── pyproject.toml       # Packaging/metadata
└── ...

License

Apache License 2.0. See LICENSE.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured