Google의 langextract 라이브러리에 대한 FastMCP 서버. 이 서버는 Claude Code와 같은 AI 조수가 MCP 인터페이스를 통해 대용량 모델과 통합된 텍스트에서 구조화된 정보를 추출할 수 있습니다.

개요

LangExtract는 텍스트 문서에서 구조화된 정보를 추출하기 위해 LLM을 사용하는 Python 라이브러리입니다. 이 MCP 서버는 Model Context Protocol을 통해 langextract의 기능을 노출합니다. 서버에는 지능형 캐싱, 지속적인 연결 및 서버 측 자격 관리가 포함되어 있으며 Claude Code와 같은 장기간의 환경에서 최적의 성능을 제공합니다.

Claude Code에 대한 빠른 설정

# 필수품

• Claude Code 설치 및 구성
• Google Gemini API 키 (여기를 얻으십시오)
• Python 3.10 이상

설치

내장 MCP 관리를 사용하여 Claude Code에 직접 설치하십시오.

서버는 자동으로 시작되며 Claude Code와 통합됩니다. 추가 설정이 필요하지 않습니다.

인증

설치 후, Claude Code에 들어가는 통합 확인:

서버가 실행되고 서버가 도구 내용을 볼 수 있도록 출력을 볼 수 있습니다.

LangExtract MCP Server

A FastMCP server for Google’s langextract library. This server enables AI assistants like Claude Code to extract structured information from unstructured text using Large Language Models through a MCP interface.

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.

Quick Setup for Claude Code

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 entering in Claude Code:

/mcp

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

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 out the resources available to the client under src/langextract_mcp/resources

Usage Examples

I am currently adding the abilty for MCP clients to pass file paths to unstructured text.

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