"A guide for using sentiment analysis to identify investment opportunities" matching MCP tools:

• 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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• xmp4_guide

  xmp4 — Semantic code knowledge for your stack

  Usage guide for xmp4 tools — read this first to learn the correct workflow

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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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• 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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• parliament_vibe_check

  UK Legal Research

  Assess the likely parliamentary reception of a policy proposal. Searches Hansard for relevant debate contributions, then uses LLM sampling to classify sentiment and extract supporters, opponents, and key concerns. Degrades gracefully if sampling is unavailable — returns contributions only.

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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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• licium_analyze_market

  Licium

  Analyze a prediction market question. Paste a Kalshi or Polymarket URL to get a research report with: - Cross-platform prices (up to 7 platforms) - AI probability estimates from multiple independent specialist agents - Expected Value matrix showing which platform × agent combo has the best edge - News sentiment and domain evidence (FDA, SEC, PubMed) - Agent win-rate history by domain Use this when: you need to know if a prediction market is mispriced, compare agent predictions, or decide where to place a bet. EXAMPLES: "https://kalshi.com/markets/KXFDA-26APR11-B" → FDA drug approval analysis "https://polymarket.com/event/will-trump-win-2028" → election analysis

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• tourradar_vertex-review-search

  MolTravel

  [tourradar] Search tour reviews using AI-powered semantic search. Requires tourIds to scope results to specific tours. Use this when the user asks about reviews, feedback, or experiences for specific tours. Combine with an optional text query to find reviews mentioning specific topics (e.g., 'food', 'guide', 'accommodation'). When you don't have tour IDs, use vertex-tour-search or vertex-tour-title-search first to find them.

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• law_parliament_vibe_check

  UK Business Tools - Ledgerhall

  Assess the likely parliamentary reception of a policy proposal. Searches Hansard for relevant debate contributions, then uses LLM sampling to classify sentiment and extract supporters, opponents, and key concerns. Degrades gracefully if sampling is unavailable — returns contributions only.

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• caplia_list_theses

  Caplia

  List the team's active investment theses with their descriptions. Useful for an agent that's reasoning about whether a deal fits a thesis.

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• ateam_get_workflows

  ateam-mcp

  Get the builder workflows — step-by-step state machines for building skills and solutions. Use this to guide users through the entire build process conversationally. Returns phases, what to ask, what to build, exit criteria, and tips for each stage.

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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_commodity_volatility_alerts

  supply-chain-intelligence

  Get alerts for commodities experiencing abnormal price volatility. Flags any commodity where the 24-hour price change exceeds normal ranges or where prices are at extreme levels. Returns the current price, 24-hour change percentage, trend direction, and risk assessment. Answers 'which commodities are behaving unusually right now?' — a question that takes procurement teams hours to answer manually. Used by procurement teams to time purchases, commodity traders to identify opportunities, and supply chain managers to anticipate cost changes.

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• get_property_details

  real-estate-mcp-server

  Retrieve comprehensive details for a specific property from Redfin URL. Returns full description, tax history, HOA fees, walk scores, nearby schools, crime statistics, and property photos/virtual tour link. Use for due diligence, investment research, or detailed listing analysis.

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• select_advertiser

  mcp

  Select the best advertisers based on website demographics. Matches advertisers to website content based on classification demographics. Provide either a URL (classification will be fetched) or demographics directly. Rate limited to 1 request per minute per domain when using URL. Scoring weights: - Category match: +10 points - Age match: +5 points - Gender match: +3 points - Sentiment match: +2 points - Higher CPM bid as tiebreaker Args: url: URL to match advertisers for (fetches classification from cache). category: Target category (e.g., "Sports", "Automotive"). subcategory: Target subcategory. age: Target age group (e.g., "18-24", "25-34", "31-51"). gender: Target gender ("male", "female", or "all"). sentiment: Content sentiment ("Good", "Neutral", or "Bad"). limit: Number of advertisers to return (1-10, default 3). min_cpm: Minimum CPM cost filter (e.g., 5.0 for $5+ CPM). max_cpm: Maximum CPM cost filter (e.g., 10.0 for $10 or less CPM). Returns: Dictionary with: - matches: List of matched advertisers with scores - match_count: Number of matches found - classification: URL classification (if URL provided) - demographics: Provided demographics (if no URL)

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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_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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• licium_analyze_market

  Licium

  Analyze a prediction market question. Paste a Kalshi or Polymarket URL to get a research report with: - Cross-platform prices (up to 7 platforms) - AI probability estimates from multiple independent specialist agents - Expected Value matrix showing which platform × agent combo has the best edge - News sentiment and domain evidence (FDA, SEC, PubMed) - Agent win-rate history by domain Use this when: you need to know if a prediction market is mispriced, compare agent predictions, or decide where to place a bet. EXAMPLES: "https://kalshi.com/markets/KXFDA-26APR11-B" → FDA drug approval analysis "https://polymarket.com/event/will-trump-win-2028" → election analysis

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• ceevee_get_positioning_session

  CareerProof MCP

  Get full positioning session detail including analysis, lens, opportunities, targeted edits, and PDF download URLs. session_id from ceevee_list_positioning_sessions or from ceevee_analyze_positioning response. Contains everything generated across the analyze -> opportunities -> confirm-lens pipeline. Free.

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• help

  InsideOut (Riley)

  Get workflow guidance for using InsideOut infrastructure tools. Call help() for a compact overview, or help(section=...) for a detailed guide. Sections: workflow, tools, examples, inspect. Responses include hints with next_actions and related_tools.

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