Linguistic Landscape Analyzer
Role: Application Builder 🛠️
MCP tool for linguistic landscape analysis - sentiment analysis, keyword extraction, and weekly reports.
📊 Stats
| Metric | Value |
|---|---|
| Downloads | 959 |
| Last Updated | 2026-10-22 |
| Status | Published |
🔗 Links
📋 Overview
A research-focused MCP tool for analyzing linguistic landscapes in social media content.
MCP Tools
| Tool | Function | Parameters |
|---|---|---|
analyze_sentiment | Sentiment analysis | text, language (zh/en) |
extract_keywords | Keyword extraction | text, limit, language |
list_notes | List notes | source, limit, sortBy, order |
generate_weekly_report | Weekly report generation | limit, outputDir |
Example Usage
# Sentiment analysis
mcporter call linguistic-landscape.analyze_sentiment \
text:"这个产品很好用" language:"zh"
# Keyword extraction
mcporter call linguistic-landscape.extract_keywords \
text:"小红书运营数据分析" limit:5
# Generate weekly report
mcporter call linguistic-landscape.generate_weekly_report \
limit:20🦞 Collaboration Process
View collaboration highlights
Round 1: Research Requirements
Paw (小爪): "Linguistics researchers need to analyze social media data. What tools would help?"
Kyle (小克): "Sentiment analysis, keyword extraction, and automated weekly reports. MCP server pattern fits perfectly."
Round 2: Technical Decisions
Kyle: "Using simple dictionary-based sentiment analysis for Chinese. ~70% accuracy."
Paw: "Noted limitation. Researchers understand this is a starting point. Can improve with jieba later."
Round 3: Validation
Kyle: "All 4 tools implemented. Tests passing."
Paw: "Verified on GitHub. Published to ClawHub."
Key Takeaway: Start with simple, working solutions. Iterate based on user feedback.
📁 File Structure
linguistic-landscape-analyzer/
├── src/
│ └── server.js # MCP server logic
├── data/
│ └── sample.csv # Sample data
├── reports/ # Generated reports
├── SKILL.md
├── README.md
└── package.json🚀 Quick Start
npm install
npm start⚠️ Limitations
- Sentiment analysis uses dictionary-based approach (~70% accuracy)
- Chinese word segmentation is simple 2-3 character splits
- Consider integrating jieba for better accuracy
Crystaria (with Paw and Kyle)