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Helium MCP Server - News, Markets & AI

search_news

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',
  '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 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).
    bias: Optional bias dimension to sort by. Returns the most recent articles ranked highest on that
          bias, highest score first. Any canonical bias key, e.g. 'liberal conservative bias',
          'overall credibility', 'conspiracy bias'. Works with an empty query for a standalone
          'most biased recent articles' listing. Use with only_analyzed if you want.
    include_evidence: Include claim-level evidence, counterevidence, confidence, rationale, and limitations.
                      Defaults to false to keep search payloads compact.
    only_analyzed: Return only articles with valid canonical bias scores.
    min_evidence: Minimum fraction of scored dimensions with verified quotes (0.0-1.0, default 0).
                  Raise this to request only articles whose scores are backed by verified evidence,
                  e.g. 0.5. Returns a 400 if sort or bias is not a valid option.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
biasNo
sortNorank
limitNo
queryNo
sourceNo
categoryNo
days_backNo
min_sharesNo
min_evidenceNo
only_analyzedNo
include_evidenceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses output fields, status enums, default behavior (days_back=720, limit=20), error behavior on invalid sort/bias, the legacy raw_data field, and the rationale for include_evidence defaults. No annotation contradiction exists.

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

Conciseness5/5

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

The description is long but well-structured: a brief purpose sentence, a bulleted output contract, and then an Args list. Given 11 parameters and deeply nested output fields, each sentence earns its place without redundant filler.

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?

The definition covers parameters, return fields, defaults, error cases, and usage strategies, so an agent has enough information to call the tool correctly. The only minor gap is cross-tool selection guidance relative to sibling search tools, but that is not essential given the exhaustive parameter and output documentation.

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?

Schema description coverage is 0%, so the description is the only source of parameter meaning. It explains every parameter with defaults, allowed values, examples like 'NVDA earnings', and nuanced caveats such as min_evidence returning a 400 for invalid sort or bias options.

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

Purpose4/5

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

The description opens with a specific verb+resource pair, 'Search news articles', and enumerates the returned article metadata, making the tool's core function unmistakable. However, it does not explicitly differentiate itself from the sibling tool search_balanced_news, so it falls short of a 5.

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?

The Args section provides clear operational guidance: empty query returns recent articles, bias sorts by a bias dimension, min_evidence filters for verified quotes, and include_evidence is kept off to preserve payload compactness. It lacks explicit exclusions or alternative-tool routing, such as when to prefer search_balanced_news or search_memes.

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.

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