Logeion MCP Server

Logeion MCP Server

Enables LLMs to search and retrieve Latin word definitions and lemmas from the Logeion dictionary database, with support for automatic lemmatization via spaCy.

Category
Visit Server

README

Logeion MCP Server

License: MIT Python 3.8+ MCP Server spaCy

Logeion is a powerful dictionary for Ancient Latin and Greek, now available as an MCP (Model Context Protocol) server so that LLMs can interact with the dictionary functionality.

๐ŸŒŸ Features

  • Latin Dictionary Lookup: Search for Latin words with comprehensive definitions
  • Lemmatization Support: Automatically finds word lemmas using spaCy's Latin language model
  • SQLite Database: Fast, local database access for quick word lookups
  • MCP Integration: Seamlessly integrates with MCP-compatible clients and LLMs

๐Ÿš€ Quick Start

# Clone the repository
git clone https://github.com/yourusername/logeion-mcp-server.git
cd logeion-mcp-server

# Install dependencies
pip install -r requirements.txt

# Download spaCy model
python -m spacy download la_core_web_lg

# Run the server
python logeion.py

๐Ÿ“š Installation

Prerequisites

  • Python 3.8+
  • pip or conda

Setup

  1. Clone the repository:
git clone https://github.com/yourusername/logeion-mcp-server.git
cd logeion-mcp-server
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
  1. Download the Latin language model for spaCy:
python -m spacy download la_core_web_lg
  1. Download the database file:
    • The database file dvlg-wheel-mini.sqlite should be placed in the project root directory
    • This contains the Latin dictionary entries

๐Ÿ› ๏ธ Usage

Running the MCP Server

python logeion.py

The server runs on stdio transport by default, making it compatible with MCP clients.

MCP Tools

get_word(word: str)

Searches for a Latin word in the dictionary database.

Parameters:

  • word (str): The Latin word to search for

Returns:

  • success (bool): Whether the search was successful
  • word (str): The original search term
  • lemma (str, optional): The lemmatized form if found
  • results (list, optional): Database results if found
  • method (str): How the search was performed ("exact_match", "lemmatized", "none", "error")
  • error (str, optional): Error message if something went wrong

Example Usage:

# Search for "amare" (to love)
result = get_word("amare")

# Search for "amo" (I love) - will find the lemma "amare"
result = get_word("amo")

Database Schema

The server connects to a SQLite database with the following structure:

  • Table: Entries
  • Key Column: head - contains the Latin word forms
  • Additional columns: Various dictionary information (definitions, parts of speech, etc.)

๐Ÿงช Testing & Demo

Run Tests

python test_logeion.py

Run Demo

python demo.py

Explore Database

python explore_db.py

๐Ÿณ Docker Deployment

Quick Start with Docker

# Build and run
docker-compose up --build

# Or build manually
docker build -t logeion-mcp-server .
docker run -it --rm -v $(pwd)/dvlg-wheel-mini.sqlite:/app/dvlg-wheel-mini.sqlite:ro logeion-mcp-server

๐Ÿ—๏ธ Development

Project Structure

logeion-mcp-server/
โ”œโ”€โ”€ logeion.py          # Main MCP server implementation
โ”œโ”€โ”€ requirements.txt    # Python dependencies
โ”œโ”€โ”€ README.md          # This file
โ”œโ”€โ”€ LICENSE            # MIT License
โ”œโ”€โ”€ mcp-config.json    # MCP server configuration
โ”œโ”€โ”€ demo.py            # Demo script
โ”œโ”€โ”€ test_logeion.py    # Comprehensive test suite
โ”œโ”€โ”€ explore_db.py      # Database exploration utility
โ”œโ”€โ”€ Dockerfile         # Docker configuration
โ”œโ”€โ”€ docker-compose.yml # Docker Compose setup
โ””โ”€โ”€ venv/              # Virtual environment

Adding New Tools

To add new MCP tools, use the @mcp.tool() decorator:

@mcp.tool()
def your_new_tool(param1: str, param2: int):
    # Your tool implementation
    return {"result": "success"}

๐Ÿ“– Documentation

๐Ÿค Contributing

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

Development Setup

# Install development dependencies
pip install -r requirements.txt

# Run linting
flake8 .
black --check .
isort --check-only .

# Run tests with coverage
pytest test_logeion.py --cov=logeion

๐Ÿ“„ License

This project is licensed under the MIT License - see the LICENSE file for details.

๐Ÿ†˜ Support

๐ŸŒ Community & Social

๐Ÿ™ Acknowledgments

  • Built with the MCP (Model Context Protocol) framework
  • Uses spaCy for Latin language processing with LatinCy by Patrick Burns
  • Integrates with the Logeion Latin dictionary database
  • Inspired by classical language education and digital humanities

Note: This MCP server provides access to Latin dictionary functionality through the Model Context Protocol, enabling LLMs to perform Latin word lookups and analysis.

Made with โค๏ธ for the classical language community

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
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
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
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