Sentiment Analyzer API MCP Server

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.

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.