Langextract MCP

🚀 LangExtract MCP Server

A FastMCP server for Google's langextract library, enabling AI assistants like Claude Code to extract structured information from unstructured text via a MCP interface.

🚀 Quick Start

Prerequisites

• Claude Code installed and configured
• Google Gemini API key (Get one here)
• Python 3.10 or higher

Installation

Install directly into Claude Code using the built - in MCP management:

claude mcp add langextract-mcp -e LANGEXTRACT_API_KEY=your-gemini-api-key -- uv run --with fastmcp fastmcp run src/langextract_mcp/server.py

The server will automatically start and integrate with Claude Code, no additional configuration is required.

Verification

After installation, verify the integration by entering in Claude Code:

/mcp

You should see output indicating the server is running and can enter the server to see its tool contents.

✨ Features

Overview

LangExtract is a Python library that uses LLMs to extract structured information from text documents while maintaining precise source grounding. This MCP server exposes langextract's capabilities through the Model Context Protocol. The server includes intelligent caching, persistent connections, and server - side credential management to provide optimal performance in long - running environments like Claude Code.

Available Tools

The server provides the following tools for text extraction workflows:

• Core Extraction:
  • extract_from_text - Extract structured information from provided text
  • extract_from_url - Extract information from web content
  • save_extraction_results - Save results to JSONL format
  • generate_visualization - Create interactive HTML visualizations

For more information, you can checkout the resources available to the client under src/langextract_mcp/resources.

Use Cases

LangExtract MCP Server supports a wide range of use cases across multiple domains:

• Healthcare and Life Sciences: Extract medications, dosages, and treatment protocols from clinical notes, structure radiology and pathology reports, and process research papers or clinical trial data.
• Legal and Compliance: Extract contract terms, parties, and obligations, and analyze regulatory documents, compliance reports, and case law.
• Research and Academia: Extract methodologies, findings, and citations from papers, analyze survey responses and interview transcripts, and process historical or archival materials.
• Business Intelligence: Extract insights from customer feedback and reviews, analyze news articles and market reports, and process financial documents and earnings reports.

💻 Usage Examples

Basic Text Extraction

Ask Claude Code to extract information using natural language:

Extract medication information from this text: "Patient prescribed 500mg amoxicillin twice daily for infection"

Use these examples to guide the extraction:
- Text: "Take 250mg ibuprofen every 4 hours"
- Expected: medication=ibuprofen, dosage=250mg, frequency=every 4 hours

Advanced Configuration

For complex extractions, specify configuration parameters:

Extract character emotions from Shakespeare using:
- Model: gemini - 2.5 - pro for better literary analysis
- Multiple passes: 3 for comprehensive extraction
- Temperature: 0.2 for consistent results

URL Processing

Extract information directly from web content:

Extract key findings from this research paper: https://arxiv.org/abs/example
Focus on methodology, results, and conclusions

📚 Documentation

Supported Models

This server currently supports Google Gemini models only, optimized for reliable structured extraction with advanced schema constraints:

• gemini - 2.5 - flash - Recommended default - Optimal balance of speed, cost, and quality
• gemini - 2.5 - pro - Best for complex reasoning and analysis tasks requiring highest accuracy

The server uses persistent connections, schema caching, and connection pooling for optimal performance with Gemini models. Support for additional providers may be added in future versions.

Configuration Reference

Environment Variables

Set during installation or in server environment:

LANGEXTRACT_API_KEY=your-gemini-api-key  # Required

Tool Parameters

Configure extraction behavior through tool parameters:

{
    "model_id": "gemini - 2.5 - flash",     # Language model selection
    "max_char_buffer": 1000,            # Text chunk size
    "temperature": 0.5,                 # Sampling temperature (0.0 - 1.0)  
    "extraction_passes": 1,             # Number of extraction attempts
    "max_workers": 10                   # Parallel processing threads
}

Output Format

All extractions return consistent structured data:

{
    "document_id": "doc_123",
    "total_extractions": 5,
    "extractions": [
        {
            "extraction_class": "medication", 
            "extraction_text": "amoxicillin",
            "attributes": {"type": "antibiotic"},
            "start_char": 25,
            "end_char": 35
        }
    ],
    "metadata": {
        "model_id": "gemini - 2.5 - flash",
        "extraction_passes": 1,
        "temperature": 0.5
    }
}

📄 Support and Documentation

Primary Resources:

• LangExtract Documentation - Core library reference
• FastMCP Documentation - MCP server framework
• Model Context Protocol - Protocol specification