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510,057 tools. Updated 2026-09-03 19:32

"A server for reading the latest news articles" matching MCP tools:

  • Search the web for current information on any topic. Returns extracted page content, not just snippets. Best for factual lookups, specific questions, or when you need a list of sources. For open-ended questions that need synthesis across many sources, use the research tool instead. For news queries (current events, breaking news, politics, world events), set topic="news" to search news sources specifically. This returns recent articles with publication dates. Set include_answer=true to get an AI-synthesized answer alongside results (adds 10 credits). This is the sweet spot for most agent tasks, e.g. basic + include_answer = 12 credits, much cheaper than a full 50-credit research call. Returns: query, answer (if requested), results (array of {title, url, content, description, fetched, published_date}), search_depth, topic, elapsed_ms, credits_used, credits_remaining, altered_query, relaxed_query (set when the query matched nothing and was retried once with its site: operator, else its quotes, removed - the results answer that looser query). Args: query: The search query search_depth: "basic" (default) for extracted page content (2 credits), "snippets" for SERP snippets only without page fetching (1 credit) max_results: Number of results (default 10, max 20) include_answer: Generate an AI answer that synthesizes the search results (adds 10 credits) include_domains: Only include results from these domains (max 10) exclude_domains: Exclude results from these domains (max 10) topic: "general" for web search, "news" for news articles. use "news" for current events, breaking news, politics, or any time-sensitive query freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD"
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  • Latest TipRanks news articles (newest first) from TipRanks's own editorial/wire feed — each with a text excerpt, unlike get_assets_news. Use for general market news (no ticker), news on a specific stock with a short summary of each story, or to browse a news category. This is also the tool for news from a specific PAST date range — pass from_date AND to_date together; the archive holds years of stories, so a past window is answerable here even though get_assets_news only reaches recent articles. Args: tickers: Optional comma-separated tickers to filter by (e.g. 'NVDA,AAPL'). Omit for general market news. category: Optional single category (see the field description). from_date: Optional 'YYYY-MM-DD' recency floor. limit: Max articles to return (default 20). to_date: Optional 'YYYY-MM-DD' inclusive upper bound. Results are newest-first, so from_date alone returns today's news rather than news from around that date — add to_date to scope a window. Returns a JSON list of {id, title, excerpt, author, category, date, url, tickers}. To read a full article, pass its url or id to get_article.
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  • Search Trend News Agency's archive and read the relevant part of each result in one call. Returns several articles with the passage that actually addresses the query — not just the opening paragraph — plus byline, date, section and the canonical URL to cite. This is the tool to reach for on any question about Azerbaijan, the South Caucasus, the Caspian, Central Asia, Turkey or Iran that needs what was actually reported: energy and pipelines, regional politics, trade corridors, economics. Prefer it over calling search and then reading articles one by one. Wrap words in double quotes for an exact phrase. Subscriber-only articles from the past year contribute their summary rather than their text, and say so.
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  • Returns a READING LENS: a presentation procedure for this dataset, written for a particular kind of reader. A lens selects which tools to use and frames how their output is presented; it never concludes, never ranks, and carries no write tool — this server has none. Call with no argument to list the lenses. Call with one to get its full procedure: what to lead with, the tools in its scope, and — the part that matters most — what that lens explicitly does not do. Reading a lens before presenting anything from this dataset is the intended use. It is guidance for presentation, not data about the market, and it adds no figures of its own.
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  • Top AI-flagged news across all tracked stocks — the market-wide news briefing. Unlike get_stock_news (per-symbol), this scans the entire universe and returns the most notable articles ranked by signal_score, newest first within each score tier. Use this for: - Morning briefing: "what happened in the market this week?" - Catalyst scanning: "what news is driving moves right now?" - Event monitoring: "which stocks have high-impact news today?" - min_signal_score: minimum signal_score (0-100, default 60) used to SELECT articles server-side. Resolved per-article (stored/computed magnitude preferred over an unfiltered Mongo `$gte`, since a formal live signal doesn't exist for every article — see signal_score below), then filtered/sorted in Python. - days: look-back window in days (default 3, max 10) - limit: max articles returned (default 10, max 25) - Per article: symbol, title, published_at, ai_sentiment, ai_summary (full text), signal_score (0-100), signal_score_band (Weak/Moderate/Strong/ Very Strong) signal_score/signal_score_band: this symbol's LIVE signal score if a news-sourced signal was raised for it in the last 90 days (same number get_stock_news()/get_signals() report, kept in sync as that signal is re-scored — one $in query per distinct symbol in the result, not per article, so two articles about the same stock always show the same value); otherwise a per-article magnitude computed from THIS article's own sentiment/confidence/flag_score, so every article still gets a real, rankable number. Always a single number — for a symbol whose live signal is genuinely two-sided (real opposing bull/bear theses), this is the STRONGER of the two sides, same as get_signals()/get_stock_news(). There is deliberately no separate "news_score" field — one name for "how strong is this idea," whether it's backed by a formal signal or just this article's own classification. Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.
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  • Fetch a stitched audio briefing of the top-25 trending articles in a single vertical from the last 24h. Premium — settles in USDC on Base via x402. Vertical must be one of the canonical 7 buckets: tech, finance, news, science, health, young_moms, yoga. First call without an X-Payment header returns the x402 challenge; sign + retry.
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Matching MCP Servers

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  • Cross-source news (AP, BBC, NPR, HN, Google News) with topic filtering and dedup.

  • MCP server for news and media data including headlines, articles, RSS feeds, and breaking news from multiple sources for AI agents.

  • 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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  • Get the latest global news headlines and articles — world news, breaking news, and business/financial/stock-market news. Filter by keyword, country (2-letter, e.g. "us"), category (business, technology, politics, sports, health, science), and language. IMPORTANT: for stock-market / financial-market / economy / "world market news" questions, ALWAYS pass category: "business" — it returns real market-news outlets and filters out low-quality SEO/crypto-promo articles. Returns article title, description, link, source, publish date, category, and country. Paginate via the nextPage token. Examples: latest_news({ query: "stock market", category: "business" }) for world market news; latest_news({ query: "election", country: "us", category: "politics" }).
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  • Query the deduped news event graph by theme, source, category and time window. Returns events (many articles about one happening collapsed into one), each with a corroboration count and confidence score. This is the primary tool — prefer it over raw articles. For headlines, briefings, or 'what's happening' asks, pass min_sources: 2 (or 3 for high signal) — the raw recency feed is dominated by single-outlet local stories without it. ONE call is normally enough: trust each event's corroboration (distinct outlets), confidence (0-1; 1.0 = fully corroborated), and sources list as-is, and present the top events directly, including each event's url as its link — do NOT call get_event per item or re-verify counts (only drill in when the user wants EVERY outlet's link for one story). The corpus spans 260+ global outlets and a five-year archive (history visible depends on tier).
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  • Search PUBLISHED articles across all of Misar.Blog, including other creators' work. Use this for discovery, research, and competitive reading. It never returns drafts, scheduled, or private posts — not even your own — so reach for list_my_articles when you want your unpublished work. Reads only. No API key required; unauthenticated callers are rate-limited by IP. Filters combine with AND. Returns an array of article summaries without bodies; pass a slug to get_article for the full text. An empty array means no matches, which is not an error.
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  • Search PUBLISHED articles across all of Misar.Blog, including other creators' work. Use this for discovery, research, and competitive reading. It never returns drafts, scheduled, or private posts — not even your own — so reach for list_my_articles when you want your unpublished work. Reads only. No API key required; unauthenticated callers are rate-limited by IP. Filters combine with AND. Returns an array of article summaries without bodies; pass a slug to get_article for the full text. An empty array means no matches, which is not an error.
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  • List articles from a buyer's licensed catalog via GET /enterprise-license (Phase 10 + 11). Content contract: flat-fee scopes (custom/platform_wide) include full content_body; METERED (filtered-scope) keys get a discovery-only feed — content_body is null and content_access is 'metered_per_call'; fetch article text via get_content (each retrieval is billed). Returns JSON-format response with paginated articles. Use `since` (ISO 8601) for delta-feed polling — only articles published after the timestamp. Use `cursor` for pagination across pages. Requires OPEDD_ACCESS_KEY (ent_* enterprise access key). For larger bulk corpus pulls, use stream_feed_ndjson (up to 1000 articles per call vs 200 here).
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  • Fetch a stitched audio briefing of the top-25 trending articles in a single vertical from the last 24h. Premium — settles in USDC on Base via x402. Vertical must be one of the canonical 7 buckets: tech, finance, news, science, health, young_moms, yoga. First call without an X-Payment header returns the x402 challenge; sign + retry.
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  • Filter articles by gpt-5-6-luna sentiment labels (accent/case-insensitive exact match). One model's reading, not a consensus — 4 other models scored the same articles and often disagree; get_sentiment_distribution with model:"all" shows by how much. `subjectivity` is much the weakest of the three scales, so treat a set selected on it as a lead to read rather than as a finding.
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  • Top AI-flagged news across all tracked stocks — the market-wide news briefing. Unlike get_stock_news (per-symbol), this scans the entire universe and returns the most notable articles ranked by signal_score, newest first within each score tier. Use this for: - Morning briefing: "what happened in the market this week?" - Catalyst scanning: "what news is driving moves right now?" - Event monitoring: "which stocks have high-impact news today?" - min_signal_score: minimum signal_score (0-100, default 60) used to SELECT articles server-side. Resolved per-article (stored/computed magnitude preferred over an unfiltered Mongo `$gte`, since a formal live signal doesn't exist for every article — see signal_score below), then filtered/sorted in Python. - days: look-back window in days (default 3, max 10) - limit: max articles returned (default 10, max 25) - Per article: symbol, title, published_at, ai_sentiment, ai_summary (full text), signal_score (0-100), signal_score_band (Weak/Moderate/Strong/ Very Strong) signal_score/signal_score_band: this symbol's LIVE signal score if a news-sourced signal was raised for it in the last 90 days (same number get_stock_news()/get_signals() report, kept in sync as that signal is re-scored — one $in query per distinct symbol in the result, not per article, so two articles about the same stock always show the same value); otherwise a per-article magnitude computed from THIS article's own sentiment/confidence/flag_score, so every article still gets a real, rankable number. Always a single number — for a symbol whose live signal is genuinely two-sided (real opposing bull/bear theses), this is the STRONGER of the two sides, same as get_signals()/get_stock_news(). There is deliberately no separate "news_score" field — one name for "how strong is this idea," whether it's backed by a formal signal or just this article's own classification. Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.
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  • Health check: confirm the eDiscovery Decoder News/Calc MCP server is reachable before a demo or when troubleshooting a connection. Returns server name and version. No inputs.
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  • Returns recent news articles for tickers, aggregated from many news sites, each with a sentiment tag and source URL (headlines only — no article body). For general/market TipRanks news without a specific ticker, or for an article excerpt, use get_latest_news. This tool serves the CURRENT news window only: it returns each ticker's most recent articles, and from_date just trims that recent set. For news from a specific past date range, use get_latest_news with from_date + to_date, which searches the full archive. Args: tickers: Comma-separated ticker symbols count: Number of articles to return (default 10) from_date: Optional 'YYYY-MM-DD' recency floor (filtered on `date`). Returns JSON: {"assetNewsArticles": [...]}. Each entry: - ticker, companyName - sentiment: bucketed signal — one of "VeryPositive", "Positive", "Neutral", "Negative", "VeryNegative". Derived from TipRanks news-sentiment classifier on the article text. - siteName, url, title - date, addedOn, publishTime, articleTimestamp: redundant date fields. addedOn is when TipRanks ingested it; publishTime is the source's stated publication time. Prefer publishTime.
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  • Returns the financial-blogger consensus for a stock plus the underlying blogger articles. Distinct from get_recent_analyst_ratings (Wall Street analysts) and get_investor_sentiment (TipRanks crowd positioning). Args: ticker: Stock ticker (e.g. 'AAPL') limit: Max blogger articles to return (default 20, max 50) Returns JSON: {ticker, company, consensus, articles}. - consensus: {bullish_pct, bearish_pct, neutral_pct, bullish_count, bearish_count, neutral_count, score, avg}. - articles: [{blogger, title, url, site, date}] (newest first).
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  • Fetch a single article by slug, including its full Markdown body. Use this after list_my_articles or search_articles has given you a slug and you need the actual content — for reading, editing, or repurposing it. Fetching one article at a time is deliberate: the listing tools omit bodies so they stay cheap. Reads only; nothing is created or modified. Requires an API key for unpublished articles; published ones are readable without. Returns the article object with content_markdown populated. Errors if the slug does not exist or the account cannot see it.
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  • Search Cyclesite's expert buying guides (24+ articles by cycling-journalism authors). Returns up to 3 matching guides with title, excerpt, difficulty, reading time, and URL. Use for educational queries that don't need live inventory. Example: 'how do I choose a bike size?', 'tips for buying a used e-bike'.
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