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Helium MCP Server - News, Markets & AI

by io.github.connerlambden

get_bias_from_url

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',
  '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, generation and review status,
  input scope/hash/size, analysis target, quote-verification method, explicit missingness
  and evidence coverage, and case-specific limitations
- total_shares: total social shares
- wayback_link: Wayback Machine archive URL if available
- image: article image URL if available

On failure (found=false, HTTP 404):
- found: false
- message: explanation string
The URL is automatically queued for ingestion; retry after ~24 hours.

Tip: if you want source-level bias (not article-level), use get_source_bias instead.
Tip: bias_values keys here use plain-text format (e.g. 'liberal conservative bias') shared
with the other bias tools where that dimension is available.

Args:
    url: Full article URL, e.g. 'https://www.nytimes.com/2024/01/01/us/politics/example.html'.
    include_evidence: Include claim-level evidence and limitations. Defaults to true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
include_evidenceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly. It discloses success/failure response shapes, automatic ingestion on 404, retry expectations, evidence status values, score scales, and verification methods for quotes. This is far beyond minimal transparency.

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 lengthy but meticulously structured: main purpose first, then detailed return fields under success/failure, followed by practical tips and argument explanations. Every sentence adds necessary information for a complex tool with no wasted words.

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 tool's complexity, the description is complete: it enumerates all return categories, explains failure and retry, clarifies parameter semantics, and directs to sibling tools when appropriate. The presence of an output schema does not reduce the need for this description, and it exceeds that bar.

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?

Although the schema has no descriptions (0% coverage), the 'Args' section fully explains both parameters: 'url' with a concrete example and 'include_evidence' with its default and effect. The description compensates entirely for the schema's lack of detail.

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 uses a specific verb+resource ('Get bias analysis for a specific article by its URL') and clearly differentiates from the sibling tool get_source_bias. The scope is precise: article-level analysis for a direct link, without requiring prior source knowledge.

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

Usage Guidelines5/5

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

Explicitly states when to use the tool ('when you have a direct link to an article and want to know its political leaning...') and provides an alternative ('if you want source-level bias, use get_source_bias instead'). It also explains failure handling and retry timing, giving clear practical guidance.

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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