Helium MCP Server

search_balanced_news

Search AI-synthesized news stories that aggregate multiple sources with summaries, evidence breakdowns, and potential outcomes.

Instructions

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

Name	Required	Description	Default
`query`	Yes
`limit`	No
`category`	No
`days_back`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Apr 13, 2026

TDQS

A4.8/5.0

Behavior4/5

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

Since no annotations are provided, the description carries the full burden of disclosing behavior. It thoroughly describes the tool's output structure, including a list of all returned fields (title, page_url, summary, evidence, etc.) and notes nuances like 'image: story image URL (when available)' and 'rank: search relevance score'. It does not explicitly state whether the operation is read-only or mention rate limits, but the search nature is inherently non-destructive and the detailed return format gives strong 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 efficiently structured: it opens with a clear one-sentence definition, immediately differentiates from the sibling tool, then uses bullet points to enumerate the return fields and a compact 'Args:' list for parameters. Every sentence adds value without redundancy, and the front-loaded contrast with search_news ensures the most important information appears early.

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 has 4 parameters and an output schema, but the description goes beyond the schema by explaining the return objects in detail, including the exact list of story fields. It also gives parameter constraints and examples of category values. Given the tool's moderate complexity, the description is sufficiently complete—it covers what the tool does, how to use it, and what to expect in the response.

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 input schema provides only property titles with no descriptions, resulting in 0% schema description coverage. However, the tool description includes an 'Args:' section that fully specifies each parameter: query is required, limit has a range and default (1-50, default 10), category lists all allowed values ('tech', 'politics', 'markets', 'business', 'science'), and days_back explains the meaning of 0. This completely compensates for the missing schema descriptions.

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 that the tool searches Helium's balanced news stories, which are AI-synthesized articles aggregating multiple sources. It uses a specific verb ('Search') and resource ('Helium's balanced news stories'), and explicitly distinguishes it from the sibling tool search_news by contrasting the return types (synthesized stories vs. individual RSS articles). This provides unambiguous purpose and differentiation.

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 this tool with search_news: 'Unlike search_news (which returns individual RSS articles), this returns Helium's own synthesized stories...'. This clear alternative guidance tells the agent when to use this tool versus its sibling. It also describes the filtering options (category, days_back) that help tailor the query, giving further context on usage.

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

Deploy Server