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Helium MCP Server - News, Markets & AI

by io.github.connerlambden

get_historical_options_data

Get the full historical options chain for a ticker on a specific date.

Returns the complete options chain including all expirations and contracts,
with bid, ask, mid prices, greeks, and Helium's proprietary model values
(helium_theo, helium_pitm, should_i_buy, should_i_sell, terminal_buy_pl,
terminal_sell_pl, etc.) baked into each contract.

Returns:
- symbol, date, data_source ('recent' or 's3')
- num_expirations: number of distinct expiration dates
- total_contracts: total number of option contracts
- option_chain: dict keyed by expiration index, each value is a list of option contracts

Each contract includes fields like: putCall, symbol, description, bid, ask, mark,
mid_price, strikePrice, expirationDate, daysToExpiration, delta, gamma, theta, vega,
impliedVolatility, openInterest, volume, helium_theo, helium_pitm, should_i_buy,
should_i_sell, terminal_buy_pl, terminal_sell_pl, and more.

Args:
    symbol: Ticker symbol, e.g. 'AAPL', 'TSLA', 'SPY'.
    date: Date in YYYY-MM-DD format, e.g. '2026-04-10'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYes
symbolYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It usefully discloses return structure, fields, and data_source values ('recent' or 's3'), but it does not explain what those data sources mean, nor does it mention potential large response sizes, error behavior, or rate limits.

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 long but well-structured with an opening summary, a 'Returns:' section, and an 'Args:' section. It is slightly verbose with the exhaustive field list, but most content adds value for a data-heavy tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description gives a thorough picture of inputs, output structure, and included contract fields. It is mostly complete for invoking the tool and interpreting results, though the meaning of data_source and possible edge cases are left unexplained.

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 schema has 0% description coverage, but the description compensates well with concrete guidance: symbol examples ('AAPL', 'TSLA', 'SPY') and exact date format with an example ('2026-04-10'). This adds practical meaning beyond the bare schema field names.

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 opens with a clear, specific verb and resource: 'Get the full historical options chain for a ticker on a specific date.' It uniquely distinguishes this from siblings like get_option_price by emphasizing 'full historical options chain' and 'all expirations and contracts.'

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?

Usage context is implied through phrases like 'full historical options chain' and the detailed returns, but the description never explicitly states when to use this tool versus alternatives like get_option_price. No exclusions or alternative tool names are mentioned.

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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