Helium MCP Server

get_ticker

Retrieve comprehensive financial data for stocks, ETFs, or cryptocurrencies including current prices, AI-generated analysis, price forecasts, volatility metrics, and option chain information.

Instructions

Get comprehensive data for a stock, ETF, or crypto ticker.

Returns:
- ticker, name, type (e.g. 'stock', 'etf', 'crypto'), industry
- latest_price, page_url
- bullish_case, bearish_case, potential_outcomes, takeaway, analysis_date (AI-generated)
- price_forecast_days, price_forecast_percent, price_forecast_lower/upper_bound_percent (model price forecast)
- future_uncertainty_urls: dict with image URLs for future_uncertainty, term_structure, volatility_surface, return_profile (when available)
- future_uncertainty_last_updated, term_structure_last_updated
- iv_rank_percentile (0-100, IV rank over past year)
- long_vol_call, long_vol_put, short_vol_call, short_vol_put: full option pack dicts (when available)

Throws an error if the ticker is not recognized.

Args:
    ticker: Ticker symbol, e.g. 'AAPL', 'AMZN', 'BTC', 'ETH', 'SPY'.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ticker`	Yes

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

A4.2/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it returns comprehensive data, includes AI-generated and model forecast elements, provides URLs for additional resources when available, and explicitly states it throws an error for unrecognized tickers. This covers most critical aspects, though it could mention rate limits or authentication needs.

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 well-structured with clear sections (Returns, Throws, Args) and uses bullet points for readability. It is appropriately sized, but some bullet points could be more concise (e.g., listing individual fields like 'bullish_case' might be streamlined). Overall, it's efficient with minimal waste.

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 (returns diverse data types), no annotations, and an output schema present, the description is highly complete. It thoroughly details return values, error conditions, and parameter usage, compensating for the lack of annotations and low schema coverage. The output schema likely covers return structure, so the description focuses on semantics, which it does effectively.

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

Parameters4/5

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

The input schema has 0% description coverage, so the description must compensate. It adds meaningful semantics by explaining the 'ticker' parameter as a symbol for stocks, ETFs, or crypto, with examples like 'AAPL' and 'BTC'. This clarifies usage beyond the schema's basic string type, though it doesn't detail format constraints like case sensitivity.

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 specific verb ('Get') and resource ('comprehensive data for a stock, ETF, or crypto ticker'), distinguishing it from siblings like get_option_price or search_news. It explicitly lists the types of data returned, making the purpose unambiguous and distinct.

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

Usage Guidelines3/5

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

The description implies usage by specifying the types of tickers supported (stock, ETF, crypto) and noting it throws an error for unrecognized symbols. However, it lacks explicit guidance on when to use this tool versus alternatives like get_option_price or search_news, leaving some context to inference.

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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