MCP Latin Tools Server

A Model Context Protocol (MCP) server for Latin Natural Language Processing (NLP), reported speech detection, and LiLa Knowledge Base querying.

Features

Latin tokenization with enclitic -que splitting
UDPipe-based lemmatization, POS tagging, and morphological analysis
Non-finite verb identification
Reported speech detection using a fine-tuned LaBERTa transformer latin model
LiLa Knowledge Base SPARQL querying and exporting results
MCP-compatible tool interface for Claude Desktop, VS Code, and MCP Inspector

System Overview

The server provides a pipeline of interoperable MCP tools for Latin NLP and Digital Humanities workflows.

The tools are designed to be used sequentially, but may also be used independently.

Tools

| Tool | Description |--------------------------------------------------------------------------------------------------------|| tokenize_latin_text | Tokenize Latin text with sentence splitting and enclitic handling || parser | Morphological analysis and preprocessing using UDPipe || detect_reported_speech | Transformer-based reported speech detection || get_lila_lemma_info | Query the LiLa Knowledge Base for lemma information || get_lila_lemma_tokens_dataframe | Retrieve LiLa corpus token occurrences and count attestations per work || export_lila_lemma_tokens_csv | Export LiLa corpus token occurrences as a CSV file |

1. Latin NLP Parsing Pipeline

The parser tool performs:

tokenization
sentence segmentation
lemmatization
POS tagging
morphological analysis
non-finite verb identification

The tool calls the UDPipe API and uses the model:

latin-evalatin24-240520

The model was evaluated on EvaLatin campaign in 2024 and trained with on Latin Dependency Treebanks.

References

EvaLatin 2024 overview:https://aclanthology.org/2024.lt4hala-1.21/
UDPipe model repository:https://github.com/ufal/evalatin2024-latinpipe

2. Reported Speech Detection

A. Preparation Tool

The parser tool also prepares the linguistic input required by the reported speech detection model.

This preparation stage:

aligns UDPipe tokenization with the original tokens
prepares aligned linguistic features
formats the input for transformer inference

B. Reported Speech Detector

The detect_reported_speech tool performs token-level reported speech prediction.

It takes the output of the parsing/preparation stage as input and returns:

token-level predictions
confidence scores

The model is:

the first experimental Latin reported speech detection model at token level
a fine-tuned LaBERTa model for token classification

References

Hugging Face model repository:https://huggingface.co/agudei/latin-reported-speech-laberta
Paper describing the experiment:https://aclanthology.org/2026.latechclfl-1.24/

3. LiLa Knowledge Base Querying

The get_lila_lemma_info tool provides simplified access to the LiLa Knowledge Base.

The tool:

accepts a Latin lemma
performs SPARQL queries automatically
retrieves lexical and linguistic information
simplifies access to Linked Open Data resources

The tool is designed to help users interact with LiLa without manually writing SPARQL queries.

LiLa

LiLa Knowledge Base:https://lila-erc.eu/sparql/

4. LiLa Corpus Attestation Retrieval

The get_lila_lemma_tokens_dataframe tool retrieves corpus attestations linked to a Latin lemma in the LiLa Knowledge Base.

The tool:

retrieves token occurrences associated with a lemma
retrieves token URIs
retrieves work titles
computes occurrence frequencies per work
structures results as a dataframe-like output

This enables corpus-based lexical exploration and quantitative analysis of lemma attestations across Latin works.

5. LiLa CSV Export

The export_lila_lemma_tokens_csv tool exports LiLa corpus attestation results as a CSV file.

The exported CSV includes:

token forms
token URIs
work titles

The tool is designed for:

corpus analysis
spreadsheet analysis
downstream NLP workflows
Digital Humanities research pipelines

Installation

Install dependencies with:

uv sync

Running the Server

Alternative

uv run python -m mcp_latin -vv

The MCP server will run at:

http://localhost:8001/mcp

MCP Inspector

You can test the server locally with:

npx @modelcontextprotocol/inspector

Then connect Inspector to:

http://localhost:8001/mcp

Example Prompts

Tokenization

Use the MCP tool tokenize_latin_text on:
"Senatus populusque Romanus."

Parsing

Use the MCP tool parser on:
"Non potui, inquit, sustinere illud durum spectaculum."

Reported Speech Detection

Use the Latin MCP tools only.

1. Parse:
"HISPO ROMANIUS alio colore dixit illam non amore adulescentis sed odio patris sui secutam"

2. Detect reported speech.

LiLa Query

Use the MCP tool get Lila information on the  "probabilis".

LiLa Query

Use the MCP tool get occurrences of the on the lemma "probabilis" and export the results.

Development Container

A reproducible VS Code devcontainer is included in:

.devcontainer/

See:

.devcontainer/README.md

for details.

Notes

UDPipe requests require internet access.
Hugging Face model weights are downloaded automatically on first inference.
The server is designed for MCP-compatible clients such as Claude Desktop and VS Code MCP integration.

MCP Latin Tools Server