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calculate_option_price

Calculate option prices using volatility inputs to determine fair market value for trading decisions.

Instructions

Calculate option price from volatility.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
expiryYes
strikeYes
rightYes
volatilityYes
underlying_priceYes

Implementation Reference

  • src/ib_async_mcp/server.py:746-760 (handler)
    The handler function for 'calculate_option_price' which qualifies the contract and calls ib.calculateOptionPriceAsync.
    if name == "calculate_option_price":
    contract = Option(
        args["symbol"],
        args["expiry"],
        args["strike"],
        args["right"],
        "SMART",
    )
    await ib.qualifyContractsAsync(contract)
    result = await ib.calculateOptionPriceAsync(
        contract,
        args["volatility"],
        args["underlying_price"],
    )
    return serialize_object(result)
  • src/ib_async_mcp/server.py:356-370 (registration)
    Registration of the 'calculate_option_price' tool, including its input schema definition.
    Tool(
    name="calculate_option_price",
    description="Calculate option price from volatility.",
    inputSchema={
        "type": "object",
        "properties": {
            "symbol": {"type": "string"},
            "expiry": {"type": "string"},
            "strike": {"type": "number"},
            "right": {"type": "string"},
            "volatility": {"type": "number"},
            "underlying_price": {"type": "number"},
        },
        "required": ["symbol", "expiry", "strike", "right", "volatility", "underlying_price"],
    },

Tool Definition Quality

C2.7/5.0
Behavior2/5

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 but fails to deliver. It does not specify the pricing model used (Black-Scholes, Binomial, etc.), computation latency, validation behavior, or error conditions for invalid option parameters.

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 extremely concise (5 words) and front-loaded with the key action. However, given the tool's complexity (6 required parameters, financial domain) and complete lack of schema documentation, this brevency crosses into under-specification rather than optimal conciseness.

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

Completeness2/5

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

For a complex financial calculation tool with 6 required parameters, 0% schema coverage, no output schema, and no annotations, a 5-word description is severely inadequate. It fails to address domain-specific requirements like option type semantics, date formats, or output expectations.

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

Parameters2/5

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

Schema description coverage is 0% for all 6 parameters. While the description mentions 'volatility', it provides no semantic context for the other 5 required parameters (symbol, expiry, strike, right, underlying_price)—critical omissions for financial instruments where 'right' (call/put) and date formats require explanation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description provides a clear verb ('Calculate') and resource ('option price') with the key input ('volatility'), making the basic function understandable. However, it fails to explicitly distinguish from sibling tool 'calculate_implied_volatility' (the inverse operation), which is a significant gap in this context.

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

Usage Guidelines2/5

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

No guidance provided on when to use this tool versus alternatives like 'calculate_implied_volatility', or prerequisites such as requiring specific market data connections. The description is purely functional with no contextual usage advice.

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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