TipRanks

get_options_chain

Read-onlyIdempotent

Contracts at one expiration, enriched with price + OI + volume.

Each row: ticker (OCC), contract_type, strike, name, price, volume,
open_interest, open_interest_change, change_percent, day_open / high /
low, last_trade_date.

Quotes are typically delayed ~15 minutes. Greeks and implied
volatility are NOT exposed by this data source — do not fabricate
them.

Args:
    ticker: Underlying (e.g. 'AAPL').
    expiration_date: One of the dates from get_options_expirations
        (YYYY-MM-DD).
    contract_type: '' for both, or 'call' or 'put'.
    strike_gte: Filter strikes >= this value (optional).
    strike_lte: Filter strikes <= this value (optional).
    limit: Max contracts to return (default 50).

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No
`ticker`	Yes
`strike_gte`	No
`strike_lte`	No
`contract_type`	No	'call' or 'put'; omit to return both sides.
`expiration_date`	Yes	Exact expiration in YYYY-MM-DD. REQUIRED — call get_options_expirations first to discover valid dates.

TDQS

A5/5.0

Behavior5/5

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

Annotations already mark this as read-only, idempotent, and non-destructive, so the description adds value by disclosing the ~15-minute quote delay, the absence of Greeks/IV in the data source, and the exact list of returned row fields. This is rich behavioral context beyond the structured metadata.

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 compact output field list, a behavior note, and a clear Args block. Every sentence adds necessary information, with no filler or repetition. It is appropriately sized for a six-parameter tool.

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?

With no output schema, the description enumerates all return fields, covering price, volume, OI, and price range. It also provides usage context (expiration discovery), data timing (delayed quotes), and limitation warnings. The combination of annotations and description fully equips an agent to call this tool correctly.

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?

Despite schema description coverage of only 33%, the description's Args section fully explains all six parameters with examples ('AAPL'), constraints (strike filters, limit default 50), and the requirement that expiration_date comes from get_options_expirations. It more than compensates for the schema's gaps.

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 first sentence 'Contracts at one expiration, enriched with price + OI + volume' states a specific verb (get) and resource (option chain for one expiration). It clearly distinguishes from get_options_expirations (dates only) and get_options_contract (single contract) by emphasizing a full chain at a single expiration.

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 instructs to use get_options_expirations first to discover valid expiration dates, and warns that Greeks/IV are not available and must not be fabricated. This gives clear when-to-use and what-not-to-use guidance, plus a direct reference to a sibling tool.

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

A3.8/5.0

Disambiguation3/5

Many tools have overlapping purposes, e.g. get_etf_analysis vs get_etf_forecast both provide ETF analyst consensus, get_etf_holdings vs get_etf_top_stocks both list constituents, and get_portfolio_overview vs get_portfolio_performance both return returns/performance. The detailed descriptions help, but the sheer number of similar tools creates ambiguity in selection.

Naming Consistency4/5

The set is largely consistent with a 'get_' prefix and descriptive nouns (get_stock_quotes, get_crypto_quote, get_dividend_history). Minor deviations include 'list_my_portfolios' instead of 'get_my_portfolios' and singular/plural variants like get_all_commodities_quotes vs get_commodity_quote, but the pattern remains predictable.

Tool Count1/5

With 71 tools, the count far exceeds the 50+ threshold described as an extreme mismatch. Even though the server covers a broad financial domain, such a large surface is overwhelming for an agent and includes many redundant or highly specific tools that could be consolidated.

Completeness5/5

The tool set provides comprehensive coverage of TipRanks data: quotes and historical data for all major asset classes, news, earnings and economic calendars, analyst and sentiment data, financial statements, technical analysis, options, portfolios, and screeners. There are no obvious dead ends for typical financial research tasks.

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