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Helium MCP Server - News, Markets & AI

by io.github.connerlambden
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search_balanced_news

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Search Helium's balanced news stories — AI-synthesized articles that aggregate multiple sources.

Unlike search_news (which returns individual RSS articles), this returns Helium's own
synthesized stories: each one draws from multiple sources and includes an AI-written
summary, takeaway, context, evidence breakdown, potential outcomes, and relevant tickers.

Returns a list of stories, each with:
- title, simple_title, date, category
- page_url: full URL to the story on heliumtrades.com
- image: story image URL (when available)
- summary: Helium's synthesized overview
- takeaway: key conclusion
- context: background context
- evidence: numbered evidence items
- potential_outcomes: forward-looking outcomes with probabilities
- relevant_tickers: related stock tickers
- num_sources: number of source articles synthesized
- rank: search relevance score

Args:
    query: Search keywords (required).
    limit: Max results (1-50, default 10).
    category: Filter by category. One of: 'tech', 'politics', 'markets', 'business', 'science'.
    days_back: Only include stories from the last N days. 0 means no date filter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
categoryNo
days_backNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds substantial behavioral context: it explains the output is AI-synthesized from multiple sources, lists the exact return fields, and describes the nature of the content (summary, takeaway, evidence, etc.). This goes well beyond annotations and gives the agent a precise mental model of the tool's behavior.

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 well-structured: a one-line purpose, a clear sibling contrast, a list of return fields, and a parameter list. Every sentence adds value and there is no redundancy. It is front-loaded with the purpose and differentiation before diving into details.

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 tool is a search operation with an output schema, and the description covers all required context: purpose, distinction from sibling, return shape, and parameter semantics. It also mentions the date filter and category options. Nothing an agent needs to call this correctly is missing.

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 must compensate. It does so fully: it explains each parameter's meaning and constraints (query required, limit range 1-50 default 10, category enum values listed, days_back semantics with 0 meaning no filter). This is exactly what the agent needs to construct valid arguments.

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 the tool searches Helium's balanced news stories, explicitly distinguishing it from search_news which returns individual RSS articles. It names the specific resource and the unique value proposition (AI-synthesized stories from multiple sources).

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?

The description explicitly contrasts with search_news ('Unlike search_news...'), telling the agent when to use this tool instead of the sibling. This is direct, unambiguous usage guidance that selects between alternatives.

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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