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Helium MCP Server - News, Markets & AI

by io.github.connerlambden

get_source_bias

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_factful) — only present when available
- recent_articles: list of most recent articles with full article fields, bias_values,
  analysis status, and optional self-contained bias_dimensions and bias_analysis.
  Evidence quotes include verification method and exact character offsets when available.
- recent_evidence_coverage: reconciled counts for verified, unverified, partial,
  legacy-scored, and pending articles, plus evidence-bearing count and verified ratio

Throws an error if the source is not found.

Args:
    source: Source name, slug, or domain (e.g. 'Fox', 'reuters', 'bbc.co.uk').
            Partial names are accepted only when they identify one source; ambiguous input returns candidates.
    recent_articles: Number of recent articles to include (1-50, default 10).
    include_evidence: Include per-article claims, verbatim evidence, counterevidence,
                      confidence, rationale, and limitations. Defaults to false to keep
                      multi-article source payloads compact.
    include_html: Also return the original website-formatted source narrative. Defaults to false.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYes
include_htmlNo
recent_articlesNo
include_evidenceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full disclosure responsibility. It explicitly states error behavior, partial match rules, ambiguity responses, and parameter defaults. It gives a thorough account of what the tool does and what can be expected.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a clear summary, followed by organized bullets and an Args section. It is lengthy but justified by the rich return payload. Some repetition (e.g., bias_values vs bias_scores) could be trimmed, but overall structure is effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of this tool, the description leaves little uncovered: input resolution, error handling, defaults, and every returned field is explained. The existence of an output schema is noted, but the description provides extensive context on its own.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The Args section thoroughly explains each parameter beyond the bare schema: source accepts name/slug/domain with partial-match caveats, recent_articles has a range, include_evidence lists exactly what is included, and include_html clarifies the additional output. This more than compensates for 0% schema description coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get comprehensive bias analysis for a news source' with a specific verb and resource. It distinguishes from siblings by focusing on a single source's bias profile, and the extensive field list makes the scope unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly implies when to use this tool: whenever you have a source name, slug, or domain. It also explains partial name handling and ambiguity resolution. However, it does not explicitly contrast with alternatives like 'get_bias_from_url' for URL input.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but two pairs could cause confusion: get_all_source_biases vs. get_source_bias, and search_news vs. search_balanced_news. The descriptions explicitly differentiate them (list vs. single source; RSS vs. synthesized stories), so an agent can disambiguate with careful reading.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case: get_* for retrievals and search_* for searches. Any variations (e.g., get_all_source_biases vs. get_source_bias) are natural qualifiers and don't break the pattern.

Tool Count5/5

With 10 tools spanning news bias analysis, options data, ticker data, and trading strategies, the count is well-scoped. Each tool has a distinct role and the set is neither too thin nor overloaded.

Completeness4/5

The surface covers the core workflows: searching news, analyzing bias at both article and source level, retrieving market data, options chains, and strategy rankings. Minor gaps exist, such as no direct way to fetch a full article by ID or list all news sources, but these are workarounds with existing tools.

Resources