# Sentiment Analyzer API MCP server

Analyze text sentiment, emotions, confidence scores, and key phrases. x402 USDC.

## Links
- Registry page: https://www.getdrio.com/mcp/io-github-br0ski777-sentiment-analyzer
- Repository: https://github.com/Br0ski777/sentiment-analyzer-x402
- Website: https://github.com/Br0ski777/sentiment-analyzer-x402

## Install
- Endpoint: https://sentiment-analyzer.api.klymax402.com/mcp
- Auth: Not captured

## Setup notes
- Remote endpoint: https://sentiment-analyzer.api.klymax402.com/mcp

## Tools
- text_analyze_sentiment - Use this when you need to determine the emotional tone and sentiment of text. Returns structured sentiment analysis with emotion breakdown and key drivers.

1. sentiment: overall sentiment label (positive, negative, neutral)
2. confidence: confidence score 0-100
3. emotions: detected emotions with scores (joy, anger, fear, surprise, sadness)
4. keyPhrases: array of phrases driving the sentiment
5. score: numeric sentiment score from -1.0 (negative) to 1.0 (positive)

Example output: {"sentiment":"positive","confidence":87,"score":0.73,"emotions":{"joy":0.82,"surprise":0.15,"anger":0.01,"fear":0.01,"sadness":0.01},"keyPhrases":["excellent results","exceeded expectations"]}

Use this BEFORE responding to customer feedback, reviews, or social media mentions. Essential for brand monitoring, support ticket triage, and content tone analysis.

Do NOT use for summarization -- use ai_summarize_text. Do NOT use for content extraction -- use web_scrape_to_markdown. Do NOT use for text classification -- use text_classify_content. Endpoint: https://sentiment-analyzer.api.klymax402.com/mcp
- text_analyze_sentiment_batch - Use this when you need to analyze sentiment of multiple texts at once (up to 20). Returns an array of individual sentiment results in one call.

1. results: array of sentiment objects, one per input text
2. Each result contains: sentiment, confidence, score, emotions, keyPhrases
3. averageSentiment: overall average sentiment score across all texts
4. distribution: count of positive/negative/neutral texts

Example output: {"results":[{"sentiment":"positive","confidence":91,"score":0.8},{"sentiment":"negative","confidence":74,"score":-0.6}],"averageSentiment":0.1,"distribution":{"positive":1,"negative":1,"neutral":0}}

Use this FOR bulk analysis of reviews, survey responses, or social media feeds. Essential when comparing sentiment across multiple data points.

Do NOT use for single text -- use text_analyze_sentiment. Do NOT use for text classification -- use text_classify_content. Do NOT use for language detection -- use text_detect_language. Endpoint: https://sentiment-analyzer.api.klymax402.com/mcp

## Resources
Not captured

## Prompts
Not captured

## Metadata
- Owner: io.github.Br0ski777
- Version: 1.1.0
- Runtime: Sse
- Transports: HTTP
- License: Not captured
- Language: Not captured
- Stars: Not captured
- Updated: May 16, 2026
- Source: https://registry.modelcontextprotocol.io