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get_option_pressure

Identify options max-pain, gamma walls, and expected move ranges for any stock or ETF to locate price magnets and support/resistance levels before expiry.

Instructions

Retrieve options-market positioning and dealer-hedging pressure zones.

Use this tool when:

  • You want to identify max-pain price (where option sellers face least loss at expiry) as a gravitational target near expiration.

  • You need to locate gamma walls (strike clusters with large open interest) that act as price magnets or resistance/support levels.

  • You want the expected-move range implied by the options market for the current weekly/monthly expiry cycle.

Parameters

symbol : str Exchange ticker in uppercase, e.g. "AAPL", "SPY", "NVDA".

Returns

dict with keys: symbol : str — normalized ticker max_pain : float — max-pain strike price gamma_wall : float — largest gamma concentration strike expected_move : float — ±expected move in dollars for nearest expiry squeeze_target: float — upside squeeze price target expiry_date : str — target expiry date (YYYY-MM-DD) pressure_zones: list — list of significant strike/OI concentration dicts

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
Behavior4/5

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

With no annotations, the description carries full burden. It accurately describes the tool as a retrieval operation ('retrieve') and details return values. However, it does not explicitly state it is read-only or mention any limitations like data freshness or authorization requirements.

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 with a concise first sentence, bullet-point use cases, and clearly separated parameter/returns sections. Every sentence adds value with no redundancy.

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 single parameter and no output schema, the description provides comprehensive context by explaining all return keys (max_pain, gamma_wall, expected_move, etc.) and their meanings, making the tool's behavior fully understandable.

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 schema has 0% coverage and only provides type and title. The description fully compensates by specifying format (uppercase) and giving examples ('AAPL', 'SPY', 'NVDA'), adding clear meaning beyond the schema.

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 it retrieves options-market positioning and dealer-hedging pressure zones, with specific outputs like max-pain, gamma walls, and expected move. This distinguishes it from sibling tools such as analyze_stock or get_iv_radar.

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 lists three use cases (identify max-pain, locate gamma walls, get expected-move range), providing clear guidance on when to use this tool versus 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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