Claimify: Research-Based Claim Extraction via MCP

An implementation of the “Claimify” methodology for factual claim extraction, delivered as a local Model Context Protocol (MCP) server. This tool implements the multi-stage claim extraction approach detailed in the academic paper “Towards Effective Extraction and Evaluation of Factual Claims” by Metropolitansky & Larson (2025).

Read the paper here

Promps from the paper have been modified for use with Structured Outputs. THIS IS NOT AN OFFICIAL IMPLEMENTATION.

Overview

Claimify extracts verifiable, decontextualized factual claims from text using a sophisticated four-stage pipeline:

1. Sentence Splitting: Breaks text into individual sentences with surrounding context
2. Selection: Filters for sentences containing verifiable propositions, excluding opinions and speculation
3. Disambiguation: Resolves ambiguities or discards sentences that cannot be clarified
4. Decomposition: Breaks down sentences into atomic, self-contained factual claims

The tool uses OpenAI’s structured outputs feature exclusively for improved reliability and exposes its functionality through the Model Context Protocol, making it available to MCP-compatible clients like Cursor and Claude Desktop.

Features

• Research-based methodology: Implements the peer-reviewed Claimify approach
• Structured outputs: Uses OpenAI’s structured outputs for reliable, type-safe responses
• MCP integration: Seamlessly integrates with development environments
• Robust parsing: Handles various text formats including lists and paragraphs
• Context-aware: Uses surrounding sentences to resolve ambiguities
• Multi-language support: Preserves original language while extracting claims
• Resource storage: Automatically stores extracted claims as MCP resources for easy retrieval
• Comprehensive logging: Detailed logging of all LLM calls, responses, and pipeline stages
• Production-ready: Includes error handling, monitoring, and configuration management

Requirements

• OpenAI API: Requires an OpenAI API key (if your MCP host does not support sampling (Github Copilot in vsCode does))
• Compatible Model: Must use a model that supports structured outputs:
  • gpt-4o (recommended)
  • gpt-4o-mini (faster and cheaper)
• Python 3.10+: For proper type hints and Pydantic support

Quick Start

1. Installation

# Clone the repository
git clone 
cd ClaimsMCP

# Create and activate a virtual environment
python -m venv claimify-env
source claimify-env/bin/activate  # On Windows: claimify-env\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Download required NLTK data (done automatically on first run)
python -c "import nltk; nltk.download('punkt_tab')"

2. Configuration

Create a .env file in the project root:

# Copy the example file
cp env.example .env