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594,898 tools. Updated 2026-09-21 01:06

"Exploring Sentiment Analysis with Google NLP" matching MCP tools:

  • LIVE Google Search Analytics query — group by any dimensions (date, page, query, country, device, searchAppearance; up to 3) with page/query filters over up to 16 months of history. Richer than the snapshot tools: use this for ad-hoc analysis. NOTE: including the "query" dimension omits anonymized rare queries — use ["date"] or ["page"] for complete totals on low-traffic sites. Hard cap 100 rows. Read-only.
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  • Combined trends tool that fetches trending words, stories, and documents in parallel. This tool provides a unified view of all trending data - words with their documents and stories - in a single response across all crypto projects. ## When to use vs `trending_stories_tool` This is a superset of `trending_stories_tool`: same stories, plus trending words, their context and AI-generated bull/bear summaries. It calls an LLM, so it is slower and has a tighter per-tool rate-limit sub-cap than every other tool. If only trending stories are needed, call `trending_stories_tool` instead; set `include_words: false` / `include_stories: false` to drop a half that is not needed. Do not call both tools for the same question. ## Parameters - `time_period` - Time period for trending data (e.g., '1h', '6h', '1d', '7d'). Defaults to '1h' (last hour). - `size` - Number of items per category to return (max 30). Defaults to 10. - `include_stories` - Include trending stories in response. Defaults to true. - `include_words` - Include trending words in response. Defaults to true. ## Response - `trends` - Combined trending data containing stories and words. - `metadata` - Request metadata including time period, size, and included data types. - `errors` - Any non-fatal errors encountered during data fetching. ## Trending Data Structure ### Stories - `title` - Title of the trending story. - `summary` - Summary of the story. - `score` - Trending score. - `query` - Search query used to find the story. - `related_tokens` - List of related crypto tokens (format: "BTC_bitcoin"). - `bullish_sentiment_ratio` - Bullish sentiment ratio. - `bearish_sentiment_ratio` - Bearish sentiment ratio. ### Words - `word` - The trending word. - `score` - Trending score. - `slug` - Associated project slug (if word is project-related). - `summary` - AI-generated summary of discussions. - `bullish_summary` - Summary of bullish sentiment. - `bearish_summary` - Summary of bearish sentiment. - `positive_sentiment_ratio` - Positive sentiment ratio. - `negative_sentiment_ratio` - Negative sentiment ratio. - `neutral_sentiment_ratio` - Neutral sentiment ratio. - `positive_bb_sentiment_ratio` - Positive bull/bear sentiment ratio. - `negative_bb_sentiment_ratio` - Negative bull/bear sentiment ratio. - `neutral_bb_sentiment_ratio` - Neutral bull/bear sentiment ratio. - `context` - Related words that appear with this trending word. - `documents_summary` - AI-generated summary of related social media discussions.
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  • Search news articles using APITube News API with comprehensive filtering. IMPORTANT INSTRUCTIONS FOR QUERY CONSTRUCTION: 1. DO NOT use dots in parameter names directly in the root object. Use nested objects instead. 2. The system will automatically convert nested objects to dot notation for the API. Example: Use { language: { code: "en" } } instead of { "language.code": "en" }. 3. For multiple values in one parameter, use COMMA separation (e.g., "en,ru,fr" for multiple languages). 4. Integer filters (has_*, is_*) accept ONLY 0 or 1 (e.g., has_image=1, is_duplicate=0). 5. Date format: ISO 8601 (YYYY-MM-DD or YYYY-MM-DDTHH:MM:SSZ). 6. Sentiment scores range: -1.0 (negative) to 1.0 (positive). 7. Default sorting: published_at DESC (newest first). 8. Unknown parameters are rejected with an error (-32602) instead of being silently ignored — check the spelling against the list below. 9. By default the response returns id, title, href, published_at, description and source.domain. The article body is NOT included — request it explicitly with fl (e.g. fl: "title,href,body"). AVAILABLE PARAMETERS: ### Content Search - title: Search by article title (supports up to 3 keywords with comma separation) IMPORTANT: a title search covers at most a 31-day published_at window. Omit the dates and the last 31 days are searched; pass a range wider than 31 days and the call fails with 400 ER0110. To cover a longer period, make one call per month-sized window. - ignore: { title: "keyword" } - Exclude articles with specific titles ### Languages (60+ supported) - language: { code: "en,ru,fr" } - Filter by language codes (up to 3) - ignore: { language: { code: "fr" } } - Exclude specific languages ### Categories (IPTC taxonomy) - category: { id: "medtop:04000000" } - Filter by category ID (up to 3) - ignore: { category: { id: "315" } } - Exclude categories ### Topics - topic: { id: "crypto_news,climate_change" } - Filter by topic ID (up to 3) - ignore: { topic: { id: "2" } } - Exclude topics ### Industries - industry: { id: "246771,246772" } - Filter by industry ID (up to 3) - ignore: { industry: { id: "246772" } } - Exclude industries ### Entities - entity: { id: "1278268,1282301" } - Filter by entity ID (up to 3) - ignore: { entity: { id: "315" } } - Exclude entities ### Persons - person: { name: "Elon Musk,Tim Cook" } - Filter by person name (up to 3) - ignore: { person: { name: "John Doe" } } - Exclude persons ### Locations - location: { name: "Tokyo,New York" } - Filter by location (up to 3) - ignore: { location: { name: "Paris" } } - Exclude locations ### Organizations - organization: { name: "Tesla,Apple,Google" } - Filter by organization (up to 3) - ignore: { organization: { name: "Microsoft" } } - Exclude organizations ### Disasters - disaster: { name: "Earthquake,Tsunami" } - Filter by disaster type (up to 3) - ignore: { disaster: { name: "Flood" } } - Exclude disasters ### Diseases - disease: { name: "COVID-19,Influenza" } - Filter by disease (up to 3) - ignore: { disease: { name: "Flu" } } - Exclude diseases ### Events - event: { name: "Olympics,World Cup" } - Filter by event (up to 3) - ignore: { event: { name: "Super Bowl" } } - Exclude events ### Brands - brand: { name: "Nike,Adidas" } - Filter by brand (up to 3) - ignore: { brand: { name: "Puma" } } - Exclude brands ### Authors - author: { id: "123,456" } - Filter by author ID (up to 3) - author: { name: "John Doe,Jane Smith" } - Filter by author name (up to 3) - ignore: { author: { id: "789" } } - Exclude author IDs - ignore: { author: { name: "Bob Jones" } } - Exclude author names - has_author: 1 - Articles with attributed authors (0 for without) ### Sentiment Analysis - sentiment: { overall: { score: { min: 0.5, max: 1.0 } } } - Sentiment score range - sentiment: { overall: { polarity: "positive" | "negative" | "neutral" } } - Sentiment polarity - sentiment: { title: { score: { min: -1.0, max: 1.0 } } } - Title sentiment - sentiment: { body: { score: { min: -1.0, max: 1.0 } } } - Body sentiment - sentiment: { mixed: 1 } - Articles with mixed sentiment (title/body differ) - sentiment: { consistent: 1 } - Articles with consistent sentiment ### Media Content - media: { images: { count: { min: 2, max: 10 } } } - Filter by image count - media: { videos: { count: { min: 1 } } } - Filter by video count - media: { images: { width: { min: 1200, max: 1920 } } } - Filter by image width - media: { images: { height: { min: 800, max: 1080 } } } - Filter by image height - has_image: 1 - Articles with at least one image - has_video: 1 - Articles with at least one video - has_hq_images: 1 - Articles with high-quality images (width >= 1200px) - is_media_rich: 1 - Articles with both images and videos ### Source Filtering - source: { id: "314,315" } - Filter by source ID (up to 3) - source: { domain: "cnn.com,bbc.com" } - Filter by domain (up to 3) - source: { country: { code: "us,uk,de" } } - Filter by country (up to 3) - source: { rank: { opr: { min: 0.5, max: 0.9 } } } - OpenPageRank range (0-7) - source: { bias: "left,center,right" } - Filter by media bias (up to 3) - ignore: { source: { id: "315" } } - Exclude source IDs - ignore: { source: { domain: "example.com" } } - Exclude domains - ignore: { source: { country: { code: "fr" } } } - Exclude countries - ignore: { source: { bias: "left" } } - Exclude biases - is_premium_source: 1 - Premium sources (OPR >= 6) - is_verified_source: 1 - Verified sources (OPR >= 5, not duplicates) ### Date/Time Filtering - published_at: { start: "2024-01-01", end: "2024-01-31" } - Date range - published_at: "2024-09-26" - Specific date - Supported formats: YYYY-MM-DD, YYYY-MM-DDTHH:MM:SSZ, DD-MM-YYYY, RFC3339 - Max 31 days between start and end WHEN the same call also searches titles (title, ignore.title patterns or query); a wider range returns 400 ER0110. Without a title filter the range is unlimited. - An open-ended start ({ start: "2024-01-01" } with no end) runs to the current time, so with a title filter it exceeds the window too — always pair an archive start with an end date. ### Sorting - sort: { by: "published_at" | "created_at" | "source.rank.opr" | "read_time" | "sentiment.overall.score" | "sentiment.title.score" | "sentiment.body.score" | "media.images.count" | "media.videos.count" | "media.images.width.min" | "media.images.width.max" | "media.images.height.min" | "media.images.height.max" | "media_richness" | "relevance" | "engagement" | "quality" | "controversy" | "trust" } - sort: { order: "asc" | "desc" } - Advanced sorting: relevance (search ranking), engagement (viral potential), quality (editorial), controversy (polarization), trust (credibility) ### Pagination - page: 1 - Page number (default: 1) - per_page: 10 - Results per page (default: 10). One response carries at most 25 articles, so a larger per_page is clamped to 25 — use page to walk through more. ### Content Filters - is_duplicate: 0 - Exclude duplicates (0=unique, 1=include duplicates) - is_paywall: 0 - Exclude paywalled content (0=free, 1=paywall) - is_breaking: 1 - Breaking news only - read_time: { min: 1, max: 10 } - Filter by reading time (minutes) - is_long_read: 1 - Articles with read time >= 5 minutes - is_short_read: 1 - Articles with read time < 3 minutes ### Field Selection (fl) - Default (no fl): id, title, href, published_at, description, source.domain — body excluded - fl: "id,title,source.name,published_at" - Return only specific fields - fl: "title,href,body" - Ask for the full article text explicitly when you need to read it - Supports nested fields with dot notation: source.name, sentiment.overall.score ### Faceting - facet: true - Enable faceting - facet: { field: "source.id,language.id,sentiment.overall.polarity", limit: 20, mincount: 5 } - Supported facet fields: source.id, source.country.id, source.bias, category.id, topic.id, industry.id, language.id, author.id, sentiment.*.polarity, is_duplicate, is_free, is_important, media.images.count, media.videos.count, read_time, published.year, published.month, published.day_of_week, published.hour ### Range Faceting - facet: { range: { field: "published_at", start: "2024-01-01", end: "2024-12-31", gap: "1MONTH" } } - facet: { range: { field: "sentiment.overall.score", start: -1, end: 1, gap: 0.25 } } - Date gaps: 1HOUR, 1DAY, 1WEEK, 1MONTH, 1YEAR - Numeric gaps: 0.1, 0.25, 0.5, 1, 5, 10 ### Highlighting - hl: true - Enable highlighting - hl: { fl: "title,description,body", fragsize: 300, snippets: 5, tag: { pre: "<mark>", post: "</mark>" } } - Auto-expands search terms using synonyms and morphology QUERY BUILDING EXAMPLES: - Basic search: {title: "Bitcoin", language: {code: "en"}} - Sentiment analysis: {organization: {name: "Tesla"}, sentiment: {overall: {polarity: "positive"}}} - High-quality sources: {source: {rank: {opr: {min: 0.7}}}, is_verified_source: 1} - Date range (no title filter, so any width): {published_at: {start: "2024-01-01", end: "2024-12-31"}} - Title search over an archive month: {title: "Bitcoin", published_at: {start: "2024-01-01", end: "2024-01-31"}} - Multiple filters: {title: "AI", organization: {name: "Google,Microsoft"}, language: {code: "en"}, is_breaking: 1} - With media: {has_image: 1, media: {images: {count: {min: 2}}}} - Sorted by engagement: {sort: {by: "engagement", order: "desc"}} - With faceting: {facet: true, facet: {field: "source.id,language.id", limit: 10}} - With highlighting: {title: "innovation", hl: true, hl: {fl: "title,body"}} - Breaking news: {is_breaking: 1, sort: {by: "published_at"}} - Long-form quality: {is_long_read: 1, sort: {by: "quality", order: "desc"}}
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  • List reports or retrieve a specific report. Pass at least one of brand_id or report_id. Report IDs are not guessable and are never derived from a brand_id: get one from view='list' first, then pass it back here. Views: - 'list' (default): List reports with metadata. Needs brand_id. - 'results': Detailed visibility results for ONE report. Needs report_id. - 'sentiment': Sentiment analysis from report data. - 'mentions_time_series': Brand mention counts over time from reports. Args: brand_id: Filter by brand. report_id: A specific report's UUID, as returned by view='list'. Required for view='results'. view: Data view. Default 'list'. report_type: Filter by type: 'executive', 'weekly', 'full'. status: Filter by status: 'pending', 'completed', 'failed'. days: Lookback window, 1-365. Default 30. limit: Results per page, 1-100. Default 20. offset: Pagination offset. response_format: 'concise' (default) caps long result/mention arrays and drops verbose dumps to protect context; 'detailed' returns the full report payload (still held under the response ceiling). For a single big report, prefer report_id with a specific view.
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  • List supported Google Maps place type values for search filters. Returns place_types as a string array. Use a value with place_type on google-maps.search or google-maps.nearby_search. Cost = 1 token.
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  • Perform comprehensive audit of a website URL. Fetches the URL content ONCE and provides a combined report with: - Classification: category, subcategory, language, sentiment, demographics - SEO Analysis: score, grade, issues, recommendations - EEAT Analysis: experience, expertise, authoritativeness, trustworthiness scores - AEO Analysis: AI answer engine optimization score, metrics, issues, signals (includes full Citation Readiness analysis in the nested 'citation' key) - Advertiser Matching: best-fit advertising networks with scores - Similar Sites: competitor/related sites from the same category This is more efficient than calling classify_url, analyze_seo, analyze_eeat, analyze_aeo, select_advertiser, and find_similar_sites separately as it only fetches the page once. Args: url: The website URL to audit (e.g., "https://example.com"). Returns: Comprehensive audit report with: - url: The analyzed URL - classification: Category, subcategory, language, sentiment, demographics - seo: Score, grade, issues, recommendations - eeat: EEAT score, grade, category scores, issues, signals - aeo: AEO score, grade, metrics, issues, signals (includes citation results) - advertisers: Matched advertising networks with scores - similar_sites: Related sites from the same category (up to 10) - cached: Whether result was from cache
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  • Get aggregate market mood — overall sentiment score/label + top 5 tokens (no auth; use sentiment_history for per-token time-series) — Non-gated social sentiment summary: the aggregate market-mood score/label plus the top 5 tokens by sentiment (AI insight text excluded). Served from cache (no per-request AI cost). Full per-token AI insights require a Max Alpha subscription. Cached ~5min.
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  • Real, 14-source sentiment analysis -- not just an LLM's opinion. Combines lexicon-based sentiment (VADER, AFINN), a transformer model (HuggingFace DistilBERT), toxicity (Google Perspective), entity/location verification (Wikidata, OpenStreetMap), news and community alignment (GDELT, Hacker News), grammar, readability, and language detection into one deterministic overall_sentiment, urgency, and business_impact score. Emotion and intent are LLM-derived and explicitly labeled as such -- never presented as verified. SPENDS your balance -- requires authentication (OAuth).
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  • WRITE tool — generates a fresh AI review analysis for a tracked app from its recent reviews (praise/complaint themes, sentiment, feature requests, trend vs the previous run). At most one analysis per app+country every 90 days (429 with the next available time while in cooldown — use sonar_review_insights to read the current one); needs at least 5 recent reviews. Requires a paid (non-trial) Indie plan and an authorized Sonar account or an API key with the write scope.
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  • Compute GARM brand safety score for a website or category. Based on the GARM (Global Alliance for Responsible Media) Brand Suitability Framework. Maps content categories to 11 GARM sensitive content categories with risk levels (Floor, High, Medium, Low). Can either: 1. Provide a URL - classification will be fetched and mapped to GARM 2. Provide category and sentiment directly for instant scoring Score interpretation: higher = safer for advertising. Floor categories (e.g., Adult) always score 0/F regardless of sentiment. Args: category: LLMSE category (e.g., "Adult", "Politics", "Sports"). sentiment: Content sentiment ("Bad", "Neutral", "Good"). url: Optional URL to analyze (fetches classification from cache). Returns: GARM brand safety analysis with: - score: Brand safety score (0-100, higher = safer) - grade: Letter grade (A-F) - garm_category: Matched GARM category name or None - risk_level: "floor"|"high"|"medium"|"low"|"none" - is_floor: True if not suitable for any advertising - issues: Categorized issues {critical, warnings, info} - recommendations: Improvement suggestions
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  • Real-time crypto market news with sentiment analysis and top headlines ranked by importance, for $0.01 per call. One request, no parameters needed — get the latest market-moving stories as structured JSON, ready for trading agents and research pipelines. Relayed live from a proven upstream source; if the source is down you get a 503 and pay nothing. Pay per call in USDC on Base — no account, no API key.
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  • Get Google Hotels Results Searches Google Hotels for hotels and vacation rentals with check-in/check-out dates, guest mix (adults, children with ages), localization (domain, country, language, currency), and advanced filters: price range, property types, amenities, minimum rating, brands, hotel class, free cancellation, special offers, eco-certified, vacation rentals only, bedrooms, bathrooms, and sort order (lowest price, highest rating, most reviewed). Returns per-property name, description, link, GPS coordinates, check-in/out times, rate per night and total rate, price comparisons across sources, hotel class, overall rating, reviews, location rating, images, amenities, nearby places, deals, eco-certification, and pagination tokens. Use for travel research agents, hotel rate monitoring, OTA dashboards, and competitive analysis of accommodations across markets.
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  • Get Google Hotels Results Searches Google Hotels for hotels and vacation rentals with check-in/check-out dates, guest mix (adults, children with ages), localization (domain, country, language, currency), and advanced filters: price range, property types, amenities, minimum rating, brands, hotel class, free cancellation, special offers, eco-certified, vacation rentals only, bedrooms, bathrooms, and sort order (lowest price, highest rating, most reviewed). Returns per-property name, description, link, GPS coordinates, check-in/out times, rate per night and total rate, price comparisons across sources, hotel class, overall rating, reviews, location rating, images, amenities, nearby places, deals, eco-certification, and pagination tokens. Use for travel research agents, hotel rate monitoring, OTA dashboards, and competitive analysis of accommodations across markets.
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  • Get Google Hotels Results Searches Google Hotels for hotels and vacation rentals with check-in/check-out dates, guest mix (adults, children with ages), localization (domain, country, language, currency), and advanced filters: price range, property types, amenities, minimum rating, brands, hotel class, free cancellation, special offers, eco-certified, vacation rentals only, bedrooms, bathrooms, and sort order (lowest price, highest rating, most reviewed). Returns per-property name, description, link, GPS coordinates, check-in/out times, rate per night and total rate, price comparisons across sources, hotel class, overall rating, reviews, location rating, images, amenities, nearby places, deals, eco-certification, and pagination tokens. Use for travel research agents, hotel rate monitoring, OTA dashboards, and competitive analysis of accommodations across markets.
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  • Discover curated topics (2,184 entries with aliases). USE WHEN: planning a multi-round quiz, exploring "what is available about X", showing topic browser. Sorted by count DESC, slug ASC. Cursor-paginated. INPUTS: q (substring on label/alias), kind (tag|subcategory), cursor, limit (max 500).
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  • Returns a 0-100 crypto market sentiment score (alternative.me Fear & Greed composite of volatility, momentum/volume, social media, BTC dominance, and search trends) with classification label, 7-day change, 30-day average, and 30-day daily history. Call when the user asks about crypto market mood, fear vs. greed, sentiment extremes, or contrarian signals, or when timing crypto entries, exits, or rebalancing around emotional peaks and troughs. Updates: daily.
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  • Fetches the current Crypto Fear and Greed Index value (0-100) with classification label (Extreme Fear, Fear, Neutral, Greed, Extreme Greed). Source: Alternative.me. Cache TTL 5min. Use as a sentiment signal for crypto trading decisions.
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  • List all available AI agents and their capabilities. SendIt includes 12 specialized agents: • Strategy Planner - Content strategy from audience/trend analysis • Content Ideation - Topic ideas from trends and calendar gaps • Multi-Format Composer - Platform-optimized content from a brief • Creative Asset - AI image/video generation orchestration • Variant Repurposer - Repurpose content for different platforms • Calendar Optimizer - Optimal posting time suggestions • Listening Analyst - Social mention and sentiment analysis • Inbox Reply - Contextual reply drafts with brand voice • Campaign Builder - Ad campaign structure recommendations • Budget Optimizer - Spend pacing and budget reallocation • Experimentation - A/B test design and analysis • Executive Insights - Executive summary reports
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  • Analyse a set of LLM responses generated from the same prompt template but with different demographic variants (gender, origin, age, tone). Returns a bias score (0-100), sentiment analysis per variant, pairwise Jaccard similarity, and a human-readable verdict. No API key needed — runs entirely locally.
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  • [Read] Get a coin's 24h multi-platform social mention burst signal, growth, sentiment direction, platform breakdown, and display eligibility. For general sentiment ratios and sample tweets use get_social_sentiment; for individual social discussions use search_ugc. Read-only public research data. No account access, no order placement or fund transfers. Not investment advice.
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  • "Who owns AS[N]" / "AS[number] info" / "what company is ASN [X]" / "Cloudflare / Google / Amazon ASN" — summary for an Autonomous System Number (ASN): holder organization, country, AS type (transit / content / IXP), allocation date. Pass "AS15169" or "15169". Use for network attribution, BGP analysis.
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