global-ai-news

news_search

Full-text search across 68,000+ AI & tech articles from 270+ curated global sources. Every article is UUID-native and carries 7 deterministic relevance scores. Search titles and summaries in any language. Paginated. Best for: finding recent developments on a topic, researching a company, product or trend, discovering what is being written about right now. Args: params (SearchInput): - query (str): Search term (any language) - limit (int): Number of results, default 10 - offset (int): Pagination offset, default 0 Returns: JSON: total_found, count, offset, has_more, next_offset, articles (id, title, teaser, source, url, published)

news_get_entity

Full entity profile: all articles, mention stats, and co-occurrence network. BrunoSan tracks 77,000+ entities across all articles. For each entity you get not just articles — but what other companies, people and products appear alongside it. This is the co-occurrence network: who moves together in the news. Best for: researching OpenAI, Google, a CEO, understanding what a company is associated with, or discovering which entities cluster together. Args: params (EntityInput): - entity (str): Entity name (e.g. 'OpenAI', 'Elon Musk', 'ChatGPT') - limit (int): Number of articles, default 10 Returns: JSON: entity, entity_type, total_mentions, articles, co_occurring_entities (name, type, count)

news_trending

Top topic clusters for a given day — what is actually moving the AI world right now. BrunoSan clusters all daily articles using deterministic heuristics (no LLM). Each cluster has a burst_score (velocity), novelty_score, and top entities. Unlike keyword trending, these are semantically coherent signal bundles. Best for: daily briefings, 'what happened in AI today?', newsletter preparation, trend detection. Args: params (TrendingInput): - date (str): Date YYYY-MM-DD, empty = latest available day - limit (int): Number of clusters, default 10 Returns: JSON: date, total_clusters, clusters (cluster_id, label, topic, event_type, article_count, cluster_label, top_entities)

news_daily_digest

Complete daily digest: all clusters, top articles, statistics, and Signal DNA for one day. The full structured overview of a day's AI news — UUID-native, auditable. Includes the URL of the published human-readable news page on brunosan.de. Ideal for morning briefings, newsletter preparation, board summaries. Signal DNA: for each top cluster you get who reported it first, how fast it spread, and whether the signal looks organic or manufactured. Also includes the URL of the published human-readable news page. Args: params (DigestInput): - date (str): Date YYYY-MM-DD, empty = latest available day Returns: str: JSON with fields: - date (str): Date of the digest - published_url (str): URL of the published page on brunosan.de - stats (dict): articles_crawled, clusters_formed, sources_active - top_clusters (list): Top 5 clusters, each with label, topic, article_count, headline, top_articles (title, source, url)

news_get_sources

Statistics and overview of all 3,300+ sources in the BrunoSan news database. Shows which domains contribute the most articles, total database stats, and daily averages. Use this to understand the data foundation behind the news answers, or to verify source diversity and coverage. Best for: 'what sources does this data come from?', transparency checks. Args: params (SourcesInput): - limit (int): Number of top sources to return, default 20 Returns: str: JSON with fields: - db_stats (dict): total_articles, unique_sources, total_entities, unique_entities, daily_avg_articles - top_sources (list): Sources with domain and article_count

news_search_entities

Search across 77,000+ known entities: companies, people, products, and regions. Shows which entities are known in the database and how often they are mentioned. Useful for discovery before using news_get_entity. Best for: 'which CEOs are most mentioned?', 'which German companies appear in AI news?', 'is [company X] in the database?' Args: params (EntitySearchInput): - query (str): Search term within entity names - type (str): Optional filter: 'company', 'person', 'product', or 'region' - limit (int): Number of results, default 20 Returns: str: JSON with fields: - query (str): Search term used - type_filter (str|null): Type filter applied - count (int): Number of entities found - entities (list): Entities with name, type, mention_count

news_signal_strength

Rank articles by 7 deterministic signal strength scores — not recency, not volume. Pure signal. Every article in BrunoSan carries 7 pre-computed relevance scores (0-100 each): AI relevance, business relevance, Mittelstand relevance, regulation relevance, risk relevance, cost relevance, operational relevance. All computed by the pipeline without LLM — reproducible, auditable, UUID-stable. This is the AI News equivalent of the Finance Intel Score and Crypto burst_score. Nobody else exposes multi-dimensional article scoring as a queryable API. Best for: 'Show me the highest-signal AI regulation articles today', 'Which articles have the strongest business relevance?', 'Risk-relevant AI news ranked by signal strength — not by clicks.' Args: params (SignalStrengthInput): - focus (str): 'ai', 'business', 'risk', 'regulation', 'cost', 'operational' - date (str): YYYY-MM-DD, empty = today - limit (int): default 10 - min_score (int): minimum score 0-100, default 60 Returns: JSON: focus, score_field, date, total_above_threshold, articles (all 7 scores, event_type, topic, url)

news_decision_maker_brief

AI news clusters ranked by decision_maker_priority_score — what a CDO, CTO or CEO needs today. Not article volume. Not recency. Priority. The score is a deterministic composite of: burst_score (velocity), official_source_count (credibility), hard_numbers_count (data density), ai_relevance_score, business_relevance_score, novelty_score. Answers: 'If a C-level decision-maker has 5 minutes, what must they read?' Nobody else has this as a queryable API. Role-based weighting available. Roles: cdo/cto → weights toward AI relevance. ceo → weights toward business relevance. compliance → weights toward compliance signal count. Best for: executive briefings, board reports, newsletter curation, 'what matters most in AI today?', agent-generated C-level summaries. Args: params (DecisionMakerInput): - date (str): YYYY-MM-DD, empty = latest available - limit (int): default 10 - role (str): optional — 'cdo', 'cto', 'ceo', 'compliance' Returns: JSON: date, role, total_clusters, brief (priority-ranked clusters with scores, entities, top articles)

news_risk_radar

Active risk and compliance signals in AI news — ranked by deterministic signal density. Surfaces what legal, compliance, and risk teams must monitor today. Formula: risk_score = risk_signal_count×2 + compliance_signal_count×3 + official_source_count×1. Compliance signals are weighted higher because they are harder to trigger. Official sources (regulators, governments, central banks) amplify the score. This is the AI News equivalent of the Regulatory MCP reg_score system — but scanning all AI news for risk signals, not just regulatory documents. Nobody else has this as a queryable API. Best for: 'What AI risk signals are active today?', 'Which AI news clusters have compliance implications?', 'Daily risk scan — regulation actions, legal actions, security incidents.' Args: params (RiskRadarInput): - date (str): YYYY-MM-DD, empty = latest available - limit (int): default 10 - min_signals (int): minimum signal count, default 1 Returns: JSON: date, scoring_formula, total_with_signals, radar (risk-ranked clusters with signal breakdown + top articles)