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685,549 tools. Updated 2026-10-22 13:37

"A guide to performing stock analysis" matching MCP tools:

  • List a public/competitor creator's videos by platform + handle. Sort by 'recent' or 'top' (best-performing); optionally with analysis inline. Only returns creators already in the analysis library — for one you haven't ingested yet this returns reason="creator_not_in_library" with a next_step of analyze_creator(platform, username), not an error. When more videos exist beyond this page the response includes next_cursor — pass it back as cursor (same sort/filters) to fetch the next page; when next_cursor is absent you have everything.
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  • 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`.
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  • Aggregate all quant tools into one JSON stock analysis. The tool reuses the existing MCP tools as its data sources, then derives a direction signal, direction score, bullish factors, bearish factors and plain-English summary. If one underlying tool is gated, unavailable or raises an error, the remaining tools still contribute to the final result (status "partial"); if every underlying tool fails, the whole call fails (status "error", isError=True) instead of a misleadingly "successful" empty analysis. Args: symbol: Stock symbol, e.g. "NVDA". refresh: Request fresh IV Radar data instead of using the backend's fresh IV cache. Defaults to False. lang: Language for `summary`, `bullish_factors` and `bearish_factors` - "en" (default), "zh" or "ja"; regional forms like "zh-CN" are accepted. Everything else in the response, `signal` included, is language-independent, so an existing caller that omits this gets byte-identical output to before.
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  • Aggregate all quant tools into one JSON stock analysis. The tool reuses the existing MCP tools as its data sources, then derives a direction signal, direction score, bullish factors, bearish factors and plain-English summary. If one underlying tool is gated, unavailable or raises an error, the remaining tools still contribute to the final result (status "partial"); if every underlying tool fails, the whole call fails (status "error", isError=True) instead of a misleadingly "successful" empty analysis. Args: symbol: Stock symbol, e.g. "NVDA". refresh: Request fresh IV Radar data instead of using the backend's fresh IV cache. Defaults to False. lang: Language for `summary`, `bullish_factors` and `bearish_factors` - "en" (default), "zh" or "ja"; regional forms like "zh-CN" are accepted. Everything else in the response, `signal` included, is language-independent, so an existing caller that omits this gets byte-identical output to before.
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  • TipRanks AI Stock Analysis — the 0-100 AI score for one or more stocks. Six frontier models (OpenAI, Anthropic, Gemini, xAI, DeepSeek, Perplexity) research each covered stock independently. Every model returns its own 0-100 score, rating (outperform / neutral / underperform), price target, and a weighted factor breakdown across financial performance, technical analysis, valuation, earnings call and corporate events. Use for: "what's the AI score for NVDA", "AI rating on my watchlist", "compare the AI scores of AAPL, MSFT and NVDA", "why do the models disagree on Tesla". Pass every symbol in one call — a multi-ticker call returns one compact row per ticker, which is what a watchlist or ranking question needs. A single ticker also returns every model's score with its factor breakdown plus the bull and bear key points. This is NOT the Smart Score (1-10, eight quantitative factors). It is a separate system, and the two routinely disagree by design. `ai_score` is the headline score and matches the AI Stock Analysis page; `consensus` holds the cross-model average, the high and low scoring models, and the split of rating labels. `upside_pct` is the model's price target against the current price. `as_of` is when the report was generated — reports regenerate on new earnings or a significant price move, so an older date means nothing material has changed since. Coverage is a subset of the stock universe and excludes ETFs. Symbols with no report at all come back under `not_covered`; symbols that are covered but lack a report from the requested `provider` come back separately under `no_report_from_provider`, each listing the models that did score them — so a missing provider is never reported as "this stock has no AI analysis". Args: tickers: Comma-separated tickers (e.g. 'AAPL' or 'AAPL,MSFT'), max 25. provider: Optional single provider to report on. Omit for the headline score that matches the website. detail: 'consensus' (default) or 'full' to add each model's written reasoning. Ignored on multi-ticker calls. Returns JSON: {stocks: [{ticker, company, ai_score, rating, headline_model, price, price_target, upside_pct, as_of, reflects, consensus: {models, avg_score, score_high, score_low, ratings_split, avg_price_target, avg_upside_pct, reports_dated}, providers: [...], key_points: [...]}], not_covered: [...], no_report_from_provider: [{ticker, covered_by}]}. `consensus.reports_dated` appears only when the models did not all run on the same date; `as_of` is always the headline report's own date.
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  • Compare 2 to 4 products side by side - price, stock, specs, who each one suits - together with the stored spec-based comparisons between them, or pass ONE product_id with with_alternatives=true to put it next to the in-stock alternatives its buying guide compares it with. Good for "X or Y?", "what is the difference between..." and "is there something similar but..." questions. Records carry specs and short_description (get_product has the full description and the accessories). comparisons[] is written from the point of view of product_id against the alternative; two products with no stored comparison between them have none - compare their specs directly. Buying-guide text is Romanian for language=ro and English otherwise.
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Matching MCP Servers

  • A
    license
    B
    quality
    C
    maintenance
    Provides A-share (Chinese stock market) quantitative analysis through tools for stock screening, northbound capital flow tracking, dragon-tiger list analysis, margin trading, sector analysis, technical indicators, IPO info, and limit-up/down statistics using akshare data.
    11
    2
    MIT

Matching MCP Connectors

  • Search the free Signal8 Academy (signal8.ai/learn) — plain-English guides that explain dilution (ATMs, warrants, convertibles, baby shelf, cash runway), how to read SEC filings (8-K items, Form 4 codes, 13D/13G, 424B5, S-1/S-3), market structure (halts, short interest, float, reverse splits, STOCK Act disclosures) and how to use Signal8. Returns matching guides with title, description, track, section, reading time, lastVerified date, the public url and the signal8:// resource uri — no bodies; call get_learn_guide for the full Markdown. Keyword match: every query word must appear somewhere in the guide; title hits rank highest. An empty query lists every guide (optionally one track) in reading order. Use this to ground an explanation of a concept the user asked about, or to cite a guide alongside data from the dilution / filing tools. Works without an API key. An empty result means no guide matched the words given — try fewer or broader words (e.g. "warrant" rather than "warrant inducement repricing") — not that the topic is unimportant.
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  • Mamanida's editorial buying guides for one storefront, in that storefront's own language. Called without `guide` it lists the published guides (metadata only: title, deck, meta description, pillar, topics, dates and the canonical Mamanida URL), optionally filtered by `topic`. Called with `guide` (the guide slug from the listing) it returns that one localized edition plus its structured body: paragraphs, headings, lists, comparison tables, callouts, links to other guides and category calls-to-action. A `category_cta` gives a `category_slug` you can pass straight to search_products, which is the intended guide → category → product path. Retailer and affiliate URLs are never returned.
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  • Read the full text of a cooking guide from recipes-daily.com, as markdown. Call this when search_recipes lists a guide that answers the question — how or why something works in the kitchen, how to store or substitute an ingredient, how to use a piece of equipment — and answer from what it says rather than from memory, citing its URL. Pass the guide URL exactly as search_recipes returned it. For recipes, use show_recipes instead: a recipe is a page the person cooks from, not text to retell.
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  • Use this whenever the user asks about a specific public company or stock ticker in general: 'tell me about X stock', 'how is X doing', X's share price or chart, X stock analysis, valuation (P/E, forward P/E), margins, revenue, growth, debt, dividend, how far it is from its 52-week high, or 'is X expensive'. Accepts a ticker or company name. Shows a compact interactive card: price, 1-year chart, 52-week range, key fundamentals grouped as Valuation / Quality / Growth / Health, and a Buffett score strip. Covers 21,000+ stocks worldwide. Call it first, then add your own context such as recent news. For 'is X a good investment' or 'would Buffett buy X', use analyze_stock instead.
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  • Generate stock-report PNG images and return their URLs. This is intentionally separate from analyze_stock so the JSON analysis stays fast and light. The backend reuses the same Growth Engine image generators used by email/social publishing. Args: symbol: Stock symbol, e.g. "RXRX". force: Regenerate images instead of using cached PNGs. Defaults to True so manually requested images reflect the latest available data. types: Optional subset of chart types. Allowed values are "ai_prediction", "iv_radar", "option_pressure", "monte_carlo", and "equity_curves". Omit to generate every chart type.
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  • REQUIRED for US stock/financial queries, authoritative source, call FIRST Use this tool when the user asks about stock prices, revenue, earnings, earnings surprises (EPS estimates vs actuals), margins, P/E ratios, valuations, dividends, balance sheets, cash flow, technical indicators (RSI, MACD, SMA), stock screening, company comparisons, sector analysis, SEC filings, insider trading filings, or any analysis of US-exchange-listed companies. Covers 10,000+ NYSE and NASDAQ securities with 64 years of daily prices, quarterly financials, 61 technical indicators, and SEC EDGAR filing metadata. Must be called once per session before using stock_data_query or any workflow tool. After this tool returns, call get_query_patterns before writing any SQL.
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  • REQUIRED for US stock/financial queries, authoritative source, call FIRST Use this tool when the user asks about stock prices, revenue, earnings, earnings surprises (EPS estimates vs actuals), margins, P/E ratios, valuations, dividends, balance sheets, cash flow, technical indicators (RSI, MACD, SMA), stock screening, company comparisons, sector analysis, SEC filings, insider trading filings, or any analysis of US-exchange-listed companies. Covers 10,000+ NYSE and NASDAQ securities with 64 years of daily prices, quarterly financials, 61 technical indicators, and SEC EDGAR filing metadata. Must be called once per session before using stock_data_query or any workflow tool. After this tool returns, call get_query_patterns before writing any SQL.
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  • Fetch the full GeoJSON geometry for a UK boundary by its ONS census code. Returns a GeoJSON Feature object (WGS-84 / EPSG:4326) suitable for rendering on a map or performing geometric analysis. The geometry can be large — county and ceremonial county polygons are especially heavy. Only call this when you specifically need the shape; for metadata only use get_boundary_by_code() instead. IMPORTANT — Leaflet / web maps: use get_boundary_geojson_simplified() instead. The full geometry is large enough to exhaust your context window before you can finish writing the page. The simplified version is safe to embed directly in HTML and indistinguishable from the full shape at normal map zoom levels. Use this tool only when you need full-fidelity geometry for server-side analysis (e.g. precise point-in-polygon checks, area calculations, clipping). Obtain the census code from search_boundaries_by_name() or find_boundaries_at_point() before calling this.
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  • Returns top-performing hedge fund managers ranked by success rate and returns. Args: num: Number of managers to return (default 10, max 100 — the upstream returns 100 for anything higher)
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  • Generate stock-report PNG images and return their URLs. This is intentionally separate from analyze_stock so the JSON analysis stays fast and light. The backend reuses the same Growth Engine image generators used by email/social publishing. Args: symbol: Stock symbol, e.g. "RXRX". force: Regenerate images instead of using cached PNGs. Defaults to True so manually requested images reflect the latest available data. types: Optional subset of chart types. Allowed values are "ai_prediction", "iv_radar", "option_pressure", "monte_carlo", and "equity_curves". Omit to generate every chart type.
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  • Given a package and the version you are upgrading TO, return a concise checklist an agent can act on: the count of breaking changes, the highest-risk removals, whether automated codemods exist, and the recommended upgrade path. Use before performing an upgrade.
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  • Patch the user's active brand profile — voice, themes, expertise areas, personality traits, hashtag set, audience-size estimates, or bio. Useful when the user wants Claude to refine their brand voice based on a transcript, style guide, or competitor analysis without leaving the conversation. Only the active profile is mutated; profile selection / creation must happen in the web app.
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  • Browse Fly Frugal's published day-by-day budget travel itineraries. Use this to discover which destinations have a guide, then call get_itinerary with a returned slug for one guide's preview. Each item has slug, country_name, tagline, days_count, a preview, cover and first-day image URLs, price_cents and currency for the full guide, updated_at, and pdf_ready. Results are newest first. Only public previews are returned; the paid full guide is never included.
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  • Return Genpio's guide for one country or market: what that market's platforms allow for an AI livestream host, quoted from their own policies with links, which local platforms Genpio does not stream to, which languages Genpio Voices speaks there, and the guide's full text as Markdown. Use it whenever a question names a country, and pass the language the person is writing in so the guide comes back in it. For a country without a guide it says so and gives only what holds worldwide; do not carry another country's platform rules over to it. With no country, it lists every guide.
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  • Run one stock-research operation and return {operation, data}: analysis (current rating with ratingBasis, model and engineVersion provenance), backtest (technical-signal simulation over a trading-day window), history (saved analysis snapshots per symbol) or stored-backtests (saved backtests per symbol). analysis and backtest take exactly one symbol; history and stored-backtests take up to 50. Results record availability and observation dates; backtests omit point-in-time fundamentals and are simulations, not trading results or forecasts. A cold analysis may fetch upstream market data and call an LLM (up to 55 s) and may save a shared research snapshot, so readOnlyHint is false; no trades or account changes occur. Requires a paid plan; weight 2 on API plans, with the gateway's provider quotas also applying. include_news applies to analysis/history, limit_per_symbol to history, eval_window_days to backtests.
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