Discover stocks that align with your investment strategy using the FMP Stock Screener API. Filter stocks based on market cap, price, volume, beta, sector, country, and more to identify the best opportunities.
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595,293 tools. Updated 2026-09-21 02:37
"A guide for using sentiment analysis to identify investment opportunities" matching MCP tools:
- Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.ConnectorNo auth
- Calculate Compound Annual Growth Rate (CAGR), real inflation-adjusted purchasing power growth (Fisher equation), and exact investment doubling time (Rule of 72 exact logarithmic solution). Behavior: Deterministic, idempotent calculation with zero external side effects. Computes Nominal CAGR = (finalValue / initialValue)^(1 / periodsYears) - 1. Computes Real CAGR using the exact Fisher relation: (1 + Nominal) / (1 + Inflation) - 1. Computes exact doubling horizon = ln(2) / ln(1 + Nominal). Returns nominal CAGR %, real CAGR %, total nominal gain, total real purchasing power gain, and doubling years. Usage Guidelines: Use for evaluating historical investment portfolio track records, business revenue growth metrics, and inflation drag analysis. Do not use for forward-looking recurring monthly investment projections; use compound_wealth or sip_investment instead.ConnectorNo auth
- Use for a ready-to-quote DESCRIPTION of DC Hub in one of four categories (general, investment, site-selection, technical), plus the live top-ranked market pocket. It does NOT compute a siting answer from free text. Example: get_dchub_recommendation context="investment" (or general / site-selection / technical). Params: context must match one of FOUR LITERAL categories - general, investment, site-selection, technical. Free text does NOT parse: you get the GENERIC blurb with matched_category=null and context_understood=false. ALWAYS returns: {recommendation:{short, medium, detailed}, matched_category, context_understood, top_pocket, related_intel, available_categories, recommendation_live, connect_url, context, success}. ADDITIONALLY on a non-matching context: is_generic_answer=true, answer_note, and next_tools naming the tool that does compute an answer. Do NOT use for a single specific lat/lon (use analyze_site), to rank by ONE criterion (use rank_markets), or for an open-ended siting question expecting a computed shortlist (use site_selection_canvas) - this tool returns descriptive copy plus a live top-pocket, not a ranked analysis.ConnectorNo auth
- Use for a ready-to-quote DESCRIPTION of DC Hub in one of four categories (general, investment, site-selection, technical), plus the live top-ranked market pocket. It does NOT compute a siting answer from free text. Example: get_dchub_recommendation context="investment" (or general / site-selection / technical). Params: context must match one of FOUR LITERAL categories - general, investment, site-selection, technical. Free text does NOT parse: you get the GENERIC blurb with matched_category=null and context_understood=false. ALWAYS returns: {recommendation:{short, medium, detailed}, matched_category, context_understood, top_pocket, related_intel, available_categories, recommendation_live, connect_url, context, success}. ADDITIONALLY on a non-matching context: is_generic_answer=true, answer_note, and next_tools naming the tool that does compute an answer. Do NOT use for a single specific lat/lon (use analyze_site), to rank by ONE criterion (use rank_markets), or for an open-ended siting question expecting a computed shortlist (use site_selection_canvas) - this tool returns descriptive copy plus a live top-pocket, not a ranked analysis.ConnectorNo auth
- Returns the complete setup and usage guide for SwapWizard. Call this FIRST before using any other tool. Covers: required configuration (API key, Alchemy RPC URL, private key), how to use poolId correctly, step-by-step operational flows for swap/zap in/zap out/analyze, transaction execution details, and approval rules.ConnectorNo auth
Matching MCP Servers
- AlicenseAqualityBmaintenanceSentiment Analysis AI - MCP server providing AI-powered tools and automation by MEOK AI Labs49 npm47 PyPIMIT
- AlicenseBqualityCmaintenanceProvides a suite of MCP tools for evidence-grounded investment research, including time-bounded search of reports and news, thesis verification against subsequent events, market response calculations, and generation of validated research briefs.9MIT
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Read-only Bicycle Guide registry: published guides, homes, taxonomy, capability spine. No auth.
Read-only Bicycle Guide registry: published guides, homes, taxonomy, capability spine. No auth.
- 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 cacheConnectorNo auth
- [Read] Search and analyze X/Twitter discussions for a topic, with tweet-level evidence and cited posts. Aggregate social mood, sentiment score, or positive/negative split -> get_social_sentiment. Open-web pages -> web_search. Multi-platform social search -> search_ugc. Read-only public research data. No account access, no order placement or fund transfers. Not investment advice.ConnectorNo auth
- Returns the complete setup and usage guide for SwapWizard. Call this FIRST before using any other tool. Covers: required configuration (API key, Alchemy RPC URL, private key), how to use poolId correctly, step-by-step operational flows for swap/zap in/zap out/analyze, transaction execution details, and approval rules.ConnectorNo auth
- 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).ConnectorNo auth
- 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.ConnectorNo auth
- 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 suggestionsConnectorNo auth
- 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.ConnectorOAuth
- Returns a list of all available product knowledge categories, each with a short description. Categories represent the main pillars of Product Thinking – Foundation, Sense, Focus, Discovery, and Delivery. Each category provides structured resources for product owners, designers, and teams, covering groundwork, user research, opportunity analysis, validation, and agile delivery. Use this tool to guide users to the right area for their current product challenge.ConnectorNo auth
- Generates a comprehensive land analysis report for a US property through one of four analytical lenses: off_grid, rural_residential, recreational, or investment. Call this when the user asks for a full analysis of a specific property. If the user's intent is unclear, ask which mode to use before calling. Returns a report ID and poll URL — the final structured report (scores, confidence ratings, narrative summary, source citations) is delivered asynchronously via polling or webhook. Consumes one analysis credit from your AcreLens account.ConnectorNo auth
- 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.ConnectorNo auth
- Broad market overview dashboard: capitalization, volume, dominance, and sentiment style metrics. No symbol required. Read-only public market breadth data. Read-only public research data. No account access, no order placement or fund transfers. Not investment advice.ConnectorNo auth
- [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.ConnectorNo auth
- Score social-post sentiment for a cashtag using a deterministic lexicon. Returns score (-1..1), positive/negative/neutral percentages, and example posts.ConnectorNo auth
- Read and analyze a specific NZX announcement. Extracts summary, key figures, sentiment, entities, and risk flags using AI. Provide the announcement_id (e.g. "12345") from search_announcements results. Returns cached results if previously extracted.ConnectorNo auth
- Aggregated real-time cryptocurrency news feed with lexicon-based sentiment analysis and cross-outlet story clustering. Filters by topic, time-range, and sentiment bands. [PAID: $0.008 USDC per call via x402 on Base. First call without payment_signature returns the payment requirements.]ConnectorNo auth