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

Recommended

uv run mcp-latin

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.