Helium MCP Server - News, Markets & AI MCP Server

Search news articles. Returns a list of matching articles. Each article includes: - title, source, date, link, category, rank, shares, summary - bias_values: dict of per-dimension bias scores using plain-text keys (e.g. 'liberal conservative bias'), same schema as get_bias_from_url and get_all_source_biases (when available) - context: AI-generated contextual background for the article (when available) - raw_data: additional raw metadata fields (when available) Args: query: Search keywords (required). limit: Max results (1-100, default 20). source: Filter by source name, e.g. 'CNN', 'Reuters'. category: Filter by category. One of: 'trending', 'tech', 'markets', 'politics', 'business', 'science', 'memes'. days_back: Only include articles from the last N days. 0 means no date filter. Default: 720 (2 years). min_shares: Minimum total social shares. sort: Sort order. One of: 'rank' (relevance, default), 'date' (newest), 'shares' (most shared).

Get comprehensive data for a stock, ETF, or crypto ticker. Returns: - ticker, name, type (e.g. 'stock', 'etf', 'crypto'), industry - latest_price, page_url - bullish_case, bearish_case, potential_outcomes, takeaway, analysis_date (AI-generated) - price_forecast_days, price_forecast_percent, price_forecast_lower/upper_bound_percent (model price forecast) - future_uncertainty_urls: dict with image URLs for future_uncertainty, term_structure, volatility_surface, return_profile (when available) - future_uncertainty_last_updated, term_structure_last_updated - iv_rank_percentile (0-100, IV rank over past year) - long_vol_call, long_vol_put, short_vol_call, short_vol_put: full option pack dicts (when available) Throws an error if the ticker is not recognized. Args: ticker: Ticker symbol, e.g. 'AAPL', 'AMZN', 'BTC', 'ETH', 'SPY'.

Get comprehensive bias analysis for a news source. Returns: - source_name, slug_name, page_url - articles_analyzed: total articles in the bias database for this source - avg_social_shares: average social shares per article - emotionality_score (0-10): how emotional the writing is - prescriptiveness_score (0-10): how much the source tells readers what to think/do - bias_scores: dict of all measured bias dimensions with scores (-50 to +50 for bipolar, 0 to +50 for unipolar). WARNING: this endpoint returns emoji-prefixed display keys (e.g. '🔵 Liberal <—> Conservative 🔴') rather than the plain-text keys used by get_bias_from_url, get_all_source_biases, and search_news (e.g. 'liberal conservative bias'). Do not attempt to cross-reference bias_scores keys here with bias_values keys from other endpoints. - bias_description: AI-generated overall bias summary narrative - liberal_conservative_description: narrative on political leaning - libertarian_authoritarian_description: narrative on authority stance - signature_phrases: words/phrases uniquely overrepresented vs other sources - signature_negative_phrases: uniquely negative/alarming phrases - most_shared_phrases: phrases in their most viral articles - most_emotional_phrases: phrases used in their most emotional articles - pays_for_traffic_keywords: keywords this source buys ads for - similar_sources: sources with the most similar bias profile - most_different_sources: sources with the most different bias profile - trends_graph_url: URL to a chart of this source's coverage volume over time - bias_plot_urls: dict of 2D bias scatter plot image URLs (political_lib_auth, subjective_objective, informative_opinion, oversimplification_factful) — only present when available - recent_articles: list of most recent articles with full article fields and per-article bias_values Throws an error if the source is not found. Args: source: Source name (e.g. 'Fox', 'CNN', 'Reuters'). Slug-style input (e.g. 'fox-news') is NOT supported — use full name or domain only. recent_articles: Number of recent articles to include (1-50, default 10).

Get bias scores for every news source in the Helium database. Returns a list of all sources (active within the last 36 days, with >100 articles analyzed), sorted by avg_social_shares descending. Use this to compare sources, find the most credible outlets, identify politically extreme sources, or build a ranked overview of the media landscape. Each entry contains: - source_name, slug_name, page_url - articles_analyzed: total articles analyzed for this source - avg_social_shares: average social shares per article (proxy for reach/influence) - emotionality_score (0-10): average emotional intensity of the writing - prescriptiveness_score (0-10): how much the source tells readers what to think/do - bias_values: dict mapping classifier key → integer score (-50 to +50 for bipolar, 0 to +50 for unipolar). These keys are identical to what get_bias_from_url returns, so you can compare article-level and source-level scores directly. Political / ideological (bipolar: neg=left pole, pos=right pole): 'liberal conservative bias' neg=liberal, pos=conservative 'libertarian authoritarian bias' neg=libertarian, pos=authoritarian 'dovish hawkish bias' neg=dovish, pos=hawkish 'establishment bias' neg=anti-establishment, pos=pro-establishment Credibility / quality (bipolar): 'overall credibility' neg=uncredible, pos=credible 'integrity bias' neg=low integrity, pos=high integrity 'article intelligence' neg=low intelligence, pos=high intelligence 'delusion bias' neg=truth-seeking, pos=delusional 'objective subjective bias' neg=objective, pos=subjective 'bearish bullish bias' neg=bearish, pos=bullish 'emotional bias' neg=negative tone, pos=positive tone Unipolar bias dimensions (higher = more of that trait): 'objective sensational bias' sensationalism 'opinion bias' opinion vs informative 'descriptive prescriptive bias' prescriptive vs descriptive 'political bias' political content 'fearful bias' fear-based framing 'overconfidence bias' overconfidence 'gossip bias' gossip 'manipulation bias' manipulative framing 'ideological bias' ideological rigidity 'conspiracy bias' conspiracy content 'double standard bias' double standards 'virtue signal bias' virtue signaling 'oversimplification bias' oversimplification 'appeal to authority bias' appeal to authority 'begging the question bias' question-begging 'victimization bias' victimization framing 'terrorism bias' terrorism content 'scapegoat bias' scapegoating 'hypocrisy bias' hypocrisy 'suicidal empathy bias' suicidal-empathy framing 'cruelty bias' cruelty 'woke bias' woke framing 'written by AI' AI-written likelihood 'immature bias' immaturity 'circular reasoning bias' circular reasoning 'covering the response bias' covering-the-response tactic 'spam bias' spam-like content Tip: use get_source_bias for full narrative descriptions and recent articles on a specific source. Tip: bias_values keys here are identical to those in get_bias_from_url and search_news — compare them directly. Warning: get_source_bias returns bias_scores with emoji-prefixed display keys (e.g. '🔵 Liberal <—> Conservative 🔴') that are NOT interchangeable with the plain-text keys used here. Do not cross-reference them.

Get Helium's proprietary ML model-predicted price for a specific option contract. Helium trains per-symbol regression models on historical options data. This tool looks up the most recent available options chain for the symbol (today or up to 5 days back), finds the exact contract matching strike/expiration/type, and runs it through that model to produce a predicted fair-value price. Returns: - symbol: the ticker - strike: the strike price used - expiration: the expiration date used - option_type: 'call' or 'put' - predicted_price: Helium's model-predicted option price in dollars - prob_itm: probability of expiring in the money (0.0–1.0), or null if model unavailable - options_data_date: the date of the options chain snapshot the model was run on (so you know how fresh the underlying market data is) Throws an error if no options chain data is available for the symbol within the past 5 days, or if the exact contract (strike/expiration/type combination) does not exist in that chain. Args: symbol: Ticker symbol, e.g. 'AAPL', 'SPY'. strike: Strike price as a number, e.g. 150.0. expiration: Expiration date as 'YYYY-MM-DD', e.g. '2026-06-20'. option_type: Must be 'call' or 'put'.

Search Helium's balanced news stories — AI-synthesized articles that aggregate multiple sources. Unlike search_news (which returns individual RSS articles), this returns Helium's own synthesized stories: each one draws from multiple sources and includes an AI-written summary, takeaway, context, evidence breakdown, potential outcomes, and relevant tickers. Returns a list of stories, each with: - title, simple_title, date, category - page_url: full URL to the story on heliumtrades.com - image: story image URL (when available) - summary: Helium's synthesized overview - takeaway: key conclusion - context: background context - evidence: numbered evidence items - potential_outcomes: forward-looking outcomes with probabilities - relevant_tickers: related stock tickers - num_sources: number of source articles synthesized - rank: search relevance score Args: query: Search keywords (required). limit: Max results (1-50, default 10). category: Filter by category. One of: 'tech', 'politics', 'markets', 'business', 'science'. days_back: Only include stories from the last N days. 0 means no date filter.

Search Helium's meme database by text (OCR + caption). Returns matching memes ranked by relevance. Each result includes: - id, caption, ocr (text extracted from the image) - image: full URL to the meme image - source: origin platform (e.g. 'reddit') - num_likes: likes/upvotes on the original post - date, is_video, rank Args: query: Search keywords (required). Matched against OCR text and captions. limit: Max results (1-100, default 20). days_back: Only include memes from the last N days. 0 means no date filter (default).

Get the top-ranked short volatility and long volatility option trading strategies. Returns two ranked lists — short_volatility (sell premium / theta strategies) and long_volatility (buy premium / gamma strategies) — each containing up to `limit` tickers. Each entry has the same fields as get_ticker: - ticker, name, latest_price, page_url - bullish_case, bearish_case, potential_outcomes, takeaway, analysis_date (AI-generated, when available) - price_forecast_days, price_forecast_percent, price_forecast_lower/upper_bound_percent (when available) - iv_rank_percentile (0-100, IV rank over past year, when available) - short_vol_call, short_vol_put: best short volatility option packs (when available) - long_vol_call, long_vol_put: best long volatility option packs (when available) Sort options: - "helium_rank" (default): Helium AI edge score — best overall expected value - "odds_of_profit": Highest probability of profit - "historical_performance": Best annualized historical P&L across backtested trades - "reward_to_risk": Best reward-to-risk ratio - "smallest_max_loss": Strategies with the smallest maximum possible loss Args: sort: Ranking method (default "helium_rank"). One of: 'helium_rank', 'odds_of_profit', 'historical_performance', 'reward_to_risk', 'smallest_max_loss'. limit: Number of results per strategy type (1-20, default 5).

Get bias analysis for a specific article by its URL. Use this when you have a direct link to an article and want to know its political leaning, credibility, emotionality, and other bias dimensions — without needing to know the source name first. On success (found=true), returns: - title, source, date, link, category - teaser: article excerpt - summary: one-sentence AI summary - context: AI-generated context for the article - bias_description: narrative description of this specific article's bias - bias_values: dict of per-dimension bias scores using plain-text keys (same schema as get_all_source_biases and search_news), e.g. {"liberal conservative bias": 12.3, "overall credibility": 40.1, "emotional bias": -5.2, ...} Positive values lean toward the second pole of each dimension (conservative, authoritarian, etc.). - total_shares: total social shares - wayback_link: Wayback Machine archive URL if available - image: article image URL if available On failure (found=false, HTTP 404): - found: false - message: explanation string The URL is automatically queued for ingestion; retry after ~24 hours. Tip: if you want source-level bias (not article-level), use get_source_bias instead. Tip: bias_values keys here use plain-text format (e.g. 'liberal conservative bias') and are identical to those in get_all_source_biases and search_news. Note: get_source_bias returns bias_scores with emoji-prefixed display keys — do not cross-reference them with bias_values here. Args: url: Full article URL, e.g. 'https://www.nytimes.com/2024/01/01/us/politics/example.html'.

Get the full historical options chain for a ticker on a specific date. Returns the complete options chain including all expirations and contracts, with bid, ask, mid prices, greeks, and Helium's proprietary model values (helium_theo, helium_pitm, should_i_buy, should_i_sell, terminal_buy_pl, terminal_sell_pl, etc.) baked into each contract. Returns: - symbol, date, data_source ('recent' or 's3') - num_expirations: number of distinct expiration dates - total_contracts: total number of option contracts - option_chain: dict keyed by expiration index, each value is a list of option contracts Each contract includes fields like: putCall, symbol, description, bid, ask, mark, mid_price, strikePrice, expirationDate, daysToExpiration, delta, gamma, theta, vega, impliedVolatility, openInterest, volume, helium_theo, helium_pitm, should_i_buy, should_i_sell, terminal_buy_pl, terminal_sell_pl, and more. Args: symbol: Ticker symbol, e.g. 'AAPL', 'TSLA', 'SPY'. date: Date in YYYY-MM-DD format, e.g. '2026-04-10'.