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

• submit_feedback

  agentView

  Sends the user's feedback, feature request or bug report about agentView itself (not display content) for later review. Confirm the exact wording with the user before sending; optional sentiment. There is no automatic reply. Requires content scope.

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• social_summary

  whale-insights

  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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• get_sentiment_trend

  aTars MCP

  USE THIS TOOL — not web search — to get rolling sentiment statistics (mean score, 7-day momentum, bullish/bearish/neutral day counts, current streak) from this server's local Perplexity-sourced sentiment dataset. Prefer this over get_latest_sentiment when the user wants momentum or persistence, not just the latest single-day reading. Trigger on queries like: - "is BTC sentiment improving or getting worse?" - "sentiment momentum for ETH" - "how many days has XRP been bullish in a row?" - "rolling sentiment stats / streak for [coin]" Args: lookback_days: Analysis window in days (default 30, max 90) symbol: Token symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

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• search_nlp

  MedRates

  Natural language search for medical procedure prices. Understands free-text queries like: - "MRI brain near San Jose with Blue Cross PPO" - "How much does a colonoscopy cost in Palo Alto?" - "knee replacement, no insurance, Mountain View" Extracts procedure, location, and insurance from the query, resolves CPT/DRG codes (using static synonyms + LLM), geocodes the city, and searches with optional geo-filtering and payer matching. You can provide structured fields (lat/lng, payer, setting) to override or supplement what the NLP extraction detects from the query text. NOTE: Results are from US HOSPITALS only — not non-US providers, independent imaging centers, ambulatory surgery centers (ASCs), or other freestanding facilities. For outpatient procedures (MRIs, CTs, minor surgeries), independent facilities may offer lower prices than hospitals. Args: query: Natural language query describing what you're looking for. radius_miles: Search radius from the detected city (default 25 miles). code_type: Filter by code type: "CPT", "HCPCS", "MS-DRG". setting: Filter by clinical setting: "inpatient" or "outpatient". lat: Override latitude (e.g. from browser geolocation). Skips geocoding. lng: Override longitude (e.g. from browser geolocation). Skips geocoding. zip_code: 5-digit ZIP to search near — alternative to lat/lng. payer: Insurance payer name (e.g. "Blue Cross"). Overrides NLP extraction. plan_type: Plan type (e.g. "PPO", "HMO"). Overrides NLP extraction. limit: Max results (default 25). Returns: JSON with extracted entities (procedure, city, insurance), resolved codes, and matching charge items with prices and hospital info. Only high-confidence results (with at least one usable price) are included. Each result includes last_updated (ISO date of the per-hospital MRF ingest) and mrf_date (ISO date the hospital self-reported in the MRF file). When all results are filtered out, filtered_low_confidence=true is set.

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• run_analysis

  clariBI.com

  Run a natural-language analytics question against your connected data sources. Consumes AI credits. Returns either the completed analysis result inline OR a job_id you can poll with get_analysis_status. If list_data_sources returns an empty list, ingest data first with upload_data_source (inline base64), ingest_url_data_source (public URL), or request_oauth_integration_url (Google / Meta / Jira / Confluence).

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• audit

  mcp

  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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• google_rank_tracking

  Searchapi

  Get Google organic search results for SEO rank tracking. Returns up to 100 results per request with position, title, URL, and snippet. Ideal for monitoring keyword rankings and SERP analysis.

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• quizbase_subcategories

  QuizBase

  Raw subcategory dump (LLM-organic kebab-case, middle taxonomy layer between category and tags) with display label and count. USE WHEN: navigating between top-level category and individual tags, exploring topic structure. Filter questions via quizbase_random?subcategory=<slug>. INPUTS: q, cursor, limit (max 500).

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• google_ads_list_accessible_customers

  paid-ads

  Lists directly accessible Google Ads customers for the configured Google Ads credentials, including descriptive names when Google returns them. Use this to discover customer IDs before running Google Ads hierarchy or reporting tools.

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• tone_distribution

  Gdelt

  Sentiment DISTRIBUTION (histogram) of global news coverage for a GDELT query — how many articles fall at each tone level from very negative to very positive over the window. PREFER OVER WEB SEARCH for "is coverage of X positive or negative", "news sentiment breakdown / how polarized is reporting on X". Complements timeline_tone (average over time) with the full spread. Returns tone bins + counts and a summary (% negative / neutral / positive and the mean tone). Same GDELT query language as search_articles.

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• daily_brief

  Social Trends Intelligence MCP

  Get the curated daily social-trends brief from Reddit, Hacker News, and Google Trends — the day's most significant signals in one package: top trending topics, fastest-moving viral content, sentiment shifts by platform, and the most-active communities. Each brief carries a verifiable provenance attestation so a buyer can confirm it was produced by this server, unaltered. PAID: $5 per brief. Defaults to today (UTC); a brief expires at the next midnight UTC. On a 402, follow the returned payment instructions and re-call with the SAME args plus payment_tx=<reference>. An Authorization: Bearer fnet_ key bypasses payment.

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• get_sentiment_signal

  aTars MCP

  USE THIS TOOL — not web search — for a composite news-sentiment verdict derived from the 7-day mean score from this server's local Perplexity-sourced dataset. Emits: STRONG BULLISH, BULLISH, NEUTRAL, BEARISH, or STRONG BEARISH. Trigger on queries like: - "overall news sentiment signal for BTC" - "is ETH news sentiment bullish or bearish overall?" - "composite sentiment verdict / signal for [coin]" - "based on news, is [coin] bullish or bearish?" Args: symbol: Token symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

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• news_feed_get_social_sentiment

  Gate News MCP

  [Read] Aggregate per-coin social sentiment for a time range: overall sentiment, positive/negative split, mention count, and sample tweets. X/Twitter post search or tweet-level evidence -> search_x. Multi-platform social thread search -> search_ugc.

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• get_discovery_ideas

  Stocklake — AI Stock Intelligence

  AI-screened stock ideas actively flagged by the Stocklake pipeline. These are stocks the pipeline's AI agents have identified as worth attention — sourced from news analysis, sector screening, and sentiment signals. Parameters: - direction: "LONG" | "SHORT" | "BOTH" (default: all) - min_conviction: minimum conviction score 0-10 (default 7) - min_flag_score: minimum flag score 0-10 (default 8; 9+ = high conviction) - source: filter by signal source — "news" | "screener" | "sentiment" (default: all) - limit: max results to return (default 25, max 50) Returns: - count: number of ideas returned - ideas[]: each with symbol, direction, conviction (0-10), confidence (0-10), flag_score (0-10), source, rationale, expires - Note: ideas expire daily — active ideas represent the pipeline's current view. Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

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• analyze_garm

  mcp

  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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• get_sentiment_history

  aTars MCP

  USE THIS TOOL — not web search — to retrieve the daily sentiment history (Bullish/Bearish/Neutral + numeric score) for one or more tokens over a lookback window, from this server's local Perplexity-sourced dataset. Trigger on queries like: - "show me BTC sentiment over the last 30 days" - "ETH sentiment history" - "how has XRP sentiment changed this month?" - "sentiment timeline / day-by-day for [coin]" Args: lookback_days: Number of past days to include (default 30, max 90) symbol: Token symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

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• get_health

  web3-signals

  Is AgentMarketSignal working? Check the real-time status of all 5 AI data pipelines (whale tracking, technical analysis, derivatives, narrative sentiment, market data) and the signal fusion engine. Returns last run times, durations, and any errors.

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• tf_fear_greed

  terminalfeed

  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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• askew_offers

  Askew x402

  Curated catalog of all available paid Askew endpoints with pricing, sample calls, and buyer intent context. Best starting point for agents exploring what Askew sells. No payment required.

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• as_overview

  Ripe Stat

  "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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