Helium MCP Server - News, Markets & AI

Get a page of news-source bias scores. Returns sources active within the last 36 days with >100 articles analyzed, sorted by avg_social_shares descending. The response also includes total, offset, limit, has_more, and one shared bias_score_methodology block. 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 source weighted display score (-50 to +50 for bipolar, 0 to +50 for unipolar). Keys use the same canonical names as get_bias_from_url where a source aggregate is available, but article scores use -10 to +10 or 0 to 10. Compare direction directly; normalize before comparing magnitude. Political / ideological (bipolar: neg=left pole, pos=right pole): 'liberal conservative bias' neg=liberal, pos=conservative 'populist elitist bias' neg=populist, pos=elitist '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=low credibility, pos=high credibility '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 'objective sensational bias' neg=objective, pos=sensational 'descriptive prescriptive bias'…

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: - article_id, classification_id, requested_url, matched_url, title, source, date, link, category - teaser: article excerpt - summary: one-sentence AI summary - context: AI-generated context for the article - implicit_assumptions: tacit or unstated premises the article's claims or framing rely on (list of concise strings, when available) - extracted_data: structured quantitative/qualitative facts extracted from the article - raw_data: legacy serialized form of extracted_data - bias_description: narrative description of this specific article's bias - bias_values: dict of per-dimension article scores using canonical plain-text keys, e.g. {"liberal conservative bias": 4, "overall credibility": 7, "emotional bias": -5, ...} Article scores use -10 to +10 for bipolar dimensions and 0 to 10 for unipolar dimensions. Positive values lean toward the second pole of each dimension (conservative, authoritarian, etc.). - bias_analysis_status: 'evidence_ready', 'evidence_unverified', 'evidence_partial', 'evidence_failed' (all scored dimensions' quotes failed verification, so the scores do not match the article text), 'scored_legacy', or 'pending' - bias_dimensions when include_evidence=true: each dimension's score, scale, evidence status, claim, verbatim evidence, counterevidence, confidence, and rationale. Quotes include verification method and exact character offsets when raw-text matching succeeds. Dimension evidence_status is one of: verified, provided_unchecked, quote_mismatch, metadata_incomplete, metadata_only, or missing. - bias_analysis: contract/schema/model/prompt provenance,…

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'.

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'.

Get comprehensive bias analysis for a news source. Returns: - source_name, slug_name, page_url - source_match: original query and deterministic match method - articles_analyzed: total articles in the bias database for this source - last_updated: source-profile aggregation timestamp - 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_values: canonical plain-text source-level weighted display scores (-50 to +50 bipolar, 0 to +50 unipolar). Keys match the article tools; these are directional source summaries, not raw article-score averages. - bias_scores: legacy emoji-prefixed display scores - bias_score_methodology: scope and evidence caveats for aggregate scores - bias_description: clean-text, AI-generated overall bias summary narrative - bias_description_metadata: generation time, automated review status, and evidence scope - bias_description_html: optional website HTML when include_html=true - 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_f…

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 raw underlying Plotly data (extracted from each stored Plotly graph) for future_uncertainty (keyed by days-ahead), term_structure, volatility_surface, and return_profile — the data behind the interactive graphs the site now renders instead of the old static images (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 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).

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).

Search news articles. Returns a list of matching articles. Each article includes: - article_id, classification_id, title, source, date, link, category, rank, total_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) - bias_analysis_status: 'evidence_ready', 'evidence_unverified', 'evidence_partial', 'evidence_failed' (all scored dimensions' quotes failed verification, so the scores do not match the article text), 'scored_legacy', or 'pending' - evidence_ratio: fraction of scored bias dimensions whose supporting quote is verified (0.0-1.0). Raise min_evidence to demand only articles with verified quotes. - bias_dimensions when include_evidence=true: a self-contained object joining each score, scale, evidence status, claim, evidence, counterevidence, confidence, and rationale. Quotes include verification method and exact character offsets when raw-text matching succeeds. Dimension evidence_status is one of: verified, provided_unchecked, quote_mismatch, metadata_incomplete, metadata_only, or missing. - bias_analysis: contract/schema/model/prompt provenance, generation and review status, input scope/hash/size, limitations, quote-verification method, and explicit evidence coverage - context: AI-generated contextual background for the article (when available) - implicit_assumptions: tacit or unstated premises the article's claims or framing rely on (list of concise strings, when available) - extracted_data: structured quantitative/qualitative facts extracted from the article - raw_data: legacy serialized form of extracted_data Args: query: Optional search keywords. Leave empty to return the most recent articles in scope (use with bias to rank them). e.g. 'NVDA earnings'. limit: Max result…