The problem Most "news bias" tools collapse a story into a single number on a left-right axis. That's useful for a thumbnail, but it's the wrong granularity for almost any real workflow - a newsroom standards editor, a fact-checker triaging a viral claim, a journalism-school instructor teaching framing, an AI safety researcher building a misinformation classifier. What those workflows actually need is structured, multi-dimensional, queryable framing data. So I built it and put it behind an MCP server that any AI assistant can call. Helium MCP scores every article on 31 dimensions. A non-exhaustive sample: liberal_conservative - the standard left-right axis (kept for compatibility) credibility - sourcing density, named-source ratio, evidence-citation pattern opinion_vs_fact - opinion language vs declarative-fact language ratio scapegoating - actor-blaming patterns vs structural-explanation patterns covering_responses - whether the article gives space to the people/orgs being criticized fearful - emotional valence, threat language sensationalism - headline-vs-body amplification, superlative density overconfidence - hedge language vs declarative certainty intelligence - reading level, conceptual density begging_the_question - assumes the conclusion in the framing oversimplification - reduces complex causation to single factors ai_authorship_probability - explicit estimate that the article was LLM-generated ... 19 more The key thing is that every dimension is operationalized - it's not just "vibes" labeling, each one is computed from features in the text. 3.2M+ articles 5,000+ sources Updated continuously Sources span global mainstream, US partisan, business press, tech press, regional, and long-tail / hyperlocal Helium MCP exposes this via three main tools you can call from any AI assistant: get_source_bias(source) - aggregate scores across a source's recent corpus get_bias_from_url(url) - score a single article on demand search_balanced_news(query) - synthesize multi-source coverage of an event with structured outcomes Add to your mcp.json in Cursor or Claude Desktop: { "mcpServers": { "helium": { "command": "npx", "args": ["-y", "mcp-remote", "https://heliumtrades.com/mcp"] } } } Free, no signup, no API key. In Claude, I asked: "Show me the bias profile for CNN's recent corpus using Helium." Real output (445 articles analyzed): Liberal/Conservative: -2 (slightly left) Credibility: 15 (moderate-high) Fearful: 4 Intelligence: 11 Covering Responses: 9 (gives space to the criticized) Opinion: 5 Overconfidence: 8 The value of seeing it as 31 numbers (and not 1) is that you can ask follow-up questions like "For these same articles, are the high-credibility ones more or less likely to be high-overconfidence?" - and the agent can compute the correlation in-place. For a newsroom standards editor: triage incoming wire/syndication content by ai_authorship_probability before it goes through your editorial pipeline. For a fact-checker: rank a list of suspect URLs by credibility (low) and overconfidence (high) - the combination is a strong indicator of claims worth investigating. For a journalism instructor: show students how the same event was framed across 10 sources, with structured scapegoating / covering_responses / opinion_vs_fact scores attached. For an AI safety researcher: the schema is essentially a deployed multi-criterion eval pipeline applied to news rather than to LLM outputs - useful as an empirical reference for how multi-criterion eval criteria interact with each other in production (Goodhart, distribution shift, taxonomy choice). For anyone building an AI agent that consumes news: structured per-source/per-article framing metadata is the missing primary key for reasoning about source reliability programmatically. In a world where readers query LLMs more than they visit homepages, the value of an individual article goes down and the value of structured, queryable, per-article metadata goes up. The schema above is one open attempt at what that metadata layer should look like. If you have ideas for dimensions that should be added (or critiques of the existing ones), I'd love to hear them - the methodology is open. This is not a substitute for human editorial judgment. Bias scoring is hard, the schema can be wrong, and there are distribution-shift / Goodhart concerns with any operationalized criterion. Use it as a triage layer, not a verdict. Repo: https://github.com/connerlambden/helium-mcp Docs + live demo: https://heliumtrades.com/mcp-page/ Companion piece on the options-pricing side: https://dev.to/connerlambden/how-i-screen-for-ratio-spread-opportunities-in-30-seconds-with-an-mcp-server-130p