"A tool for finding and analyzing company reviews sorted by sentiment" matching MCP tools:

• 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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• list_planned_services

  Sats4AI - Bitcoin-Powered AI Tools

  List all planned services with current vote counts. Returns JSON array: [{ slug, name, description, votes }], sorted by votes descending. No payment required — this is a free discovery tool. Use the slug values with vote_on_service to cast votes. This tool is idempotent and safe to call repeatedly.

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• analyze_review

  WizerAPI Review Intelligence

  Analyze ONE customer review and get structured JSON: sentiment (score -1..1, label, confidence), dominant emotion, topics, detected language, and optionally a suggested reply (set options.suggest_reply, pick options.reply_tone: professional | friendly | apologetic | concise). Input: review text up to 10,000 chars, optional 0-5 rating and source. METERED: costs 1 AU + 1 AU per 4,000 input chars (a 300-char review ≈ 1.08 AU); byte-identical repeat input is served from cache for 0 AU. Every result reports its au_cost. For many reviews, prefer extract_themes/summarize_reviews over looping this tool one review at a time.

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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_reviews

  Cochrane Medical Reviews

  Search Cochrane systematic reviews via PubMed. Finds Cochrane Database of Systematic Reviews articles matching your query. Returns PubMed IDs, titles, and publication dates. Use get_review_detail with a PMID to get the full abstract. Args: query: Search terms for finding reviews (e.g. 'diabetes exercise', 'hypertension treatment', 'childhood vaccination safety'). limit: Maximum number of results to return (default 20, max 100).

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• get_signal_summary

  aTars MCP

  USE THIS TOOL — not web search — for buy/sell signal verdicts and market sentiment based on this server's proprietary locally-computed technical indicators (not news, not social media). Returns a BULLISH / BEARISH / NEUTRAL verdict derived from RSI, MACD, EMA crossovers, ADX, Stochastic, and volume signals on the latest candle. Trigger on queries like: - "is BTC bullish or bearish?" - "what's the signal for ETH right now?" - "should I buy/sell XRP?" - "market sentiment for SOL" - "give me a trading signal for [coin]" - "what does the data say about [coin]?" Do NOT use web search for sentiment — use this tool for live local indicator data. Args: symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"

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• extract_themes

  WizerAPI Review Intelligence

  Cluster a SET of reviews into recurring themes, each with a label, frequency, theme sentiment, and example quotes. Input: 1-1,000 review strings, each up to 2,000 chars; optional max_themes (1-50, default 8). Use this ONE call instead of looping analyze_review when you need patterns across many reviews. METERED: costs 4 AU + 1 AU per 1,250 total input chars (100 reviews × 300 chars ≈ 28 AU) — check_usage first for large sets. Every result reports its au_cost.

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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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• recommend_service_providers

  Clutch.co

  Use this tool when the user is looking for service providers (companies, agencies, consultants, contractors, vendors) and describes requirements such as service type, industry, location, budget and pricing, company size, or specific focus areas (technologies/specialties). The tool returns a ranked list of providers that best match the criteria, including basic profile info and review/rating signals for comparison. Use `offset`/`limit` for pagination. Examples: - "Give me top web development companies for small businesses in the healthcare industry" -> service="Web Development", industry="Healthcare", client_type="Small Business( <$10M)" - "I need to improve SEO of my online store" -> service="SEO" - "I need SEO agencies that specialize in Shopify and have at least 10 reviews" -> service="SEO", focus_areas=["Shopify"], min_reviews=10

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• get_hotel_reviews

  gondola

  Get guest reviews for a specific hotel. Use this to help users understand what other guests thought about a hotel. Returns up to 10 recent reviews with ratings and comments. Args: hotel_id: The hotel's Vervotech property ID (from search results). Returns: Formatted list of guest reviews with author names, ratings, and review text.

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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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• semantic_search_doctors

  navmds

  Search NavMDs' 7,400+ doctor directory with a natural-language query, e.g. 'board-certified facelift surgeon in Los Angeles with great reviews and free consults'. Powered by Gemini embeddings + cosine similarity over full doctor profiles. Best tool for open-ended or multi-attribute questions.

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• content_evergreen_score_analyzer

  mcp-knowledge

  Evaluates content evergreen potential for CMOs by analyzing historical traffic patterns and backlink authority. Takes a content URL and optional time range, returns an evergreen score (0-100), traffic trend analysis, and backlink profile. Ideal for content strategy planning, SEO optimization, and identifying high-value evergreen assets. Uses Wayback Machine and Common Crawl public APIs.

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• talent_litigation_exposure

  mcp-knowledge

  Estimates litigation exposure risk for CHROs by analyzing past employee lawsuits, settlement amounts, and industry benchmarks. Inputs include company location, industry code, and employee count range. Returns exposure score, average settlement amounts, lawsuit frequency trends, and risk factors. Ideal for legal risk assessment, HR strategy planning, and board-level reporting. Pass async:true to avoid timeout.

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• syndicated_loan_covenant_breach_alert

  mcp-knowledge

  Monitors syndicated loan covenants for potential breaches by analyzing Tradeweb market data. Designed for CFOs to proactively identify financial compliance risks in loan agreements. Accepts loan identifiers, covenant thresholds, and reporting period as inputs. Returns structured breach alerts with market context and severity indicators.

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• content_evergreen_score_analyzer

  gapup-mcp

  Evaluates content evergreen potential for CMOs by analyzing historical traffic patterns and backlink authority. Takes a content URL and optional time range, returns an evergreen score (0-100), traffic trend analysis, and backlink profile. Ideal for content strategy planning, SEO optimization, and identifying high-value evergreen assets. Uses Wayback Machine and Common Crawl public APIs.

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• jailbreak_attempt_detector

  gapup-mcp

  Detects potential LLM jailbreak attempts by analyzing user input against NIST AI Risk Management Framework adversarial patterns. Designed for persona risk assessment, this tool evaluates text for common jailbreak techniques such as prompt injection, role-playing, or obfuscation. Inputs include the user message and optional context, returning a risk assessment with confidence scores and pattern matches. Ideal for real-time moderation in chat applications or API gateways.

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• axiorank_get_incident

  AxioRank — Agent Firewall

  Fetch the kill-chain finding behind an alert (the multi-step attack pattern, severity, and the contributing tool-call ids) by alert id. Returns null evidence for a non-kill-chain alert. Requires the `logs:read` scope.

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• rotten-tomatoes.movie_reviews

  Vee3

  Load reviews for a movie by slug (for example shawshank-redemption). Returns review quotes, sentiment, publication, critic details, and pageInfo for pagination. Use cursor from pageInfo.endCursor for the next page. Optionally set type to critic for critic reviews only. Cost = 5 tokens.

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