derive-options-mcp

Propose trades for a view

propose_trades

Read-only

Describe a market view and budget to receive live-priced option structures ranked by payoff per risk, sized to your budget, and ready to trade now.

Instructions

Given a view on a coin and a budget, return a few real option structures that express it, priced against the live book and sized to the budget. Use this for 'I think X will go up', 'what should I trade if I expect a big move', 'how do I bet on ETH staying flat'. Every result is tradable right now: candidates that cannot be filled are discarded, not softened. Each is ranked by what it pays if the market moves one standard deviation by that expiry — the market's own implied volatility, not a guess — divided by what it puts at risk. Structures whose loss has no limit are left out unless allow_uncapped is set.

Input Schema

TableJSON Schema

Name	Required	Description
`view`	Yes	bullish = expects it to rise; bearish = to fall; quiet = to stay near here; big-move = a large move either way.
`limit`	No
`currency`	Yes	The coin.
`budget_usd`	Yes	What they'll spend, or for a structure that pays up front, what they'll risk.
`horizon_days`	No	Roughly when they expect to be right. Default 30.
`target_price`	No	A specific level they have in mind, reported per structure.
`allow_uncapped`	No	Include structures that can lose without limit. Off by default.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`spot`	Yes
`view`	Yes
`atmIv`	Yes
`currency`	Yes
`rejected`	Yes
`budgetUsd`	Yes
`proposals`	Yes
`referenceSpot`	Yes
`referenceMovePct`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 23, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations only declare read-only and open-world behavior; the description adds essential behavioral details: unfillable candidates are discarded rather than softened, ranking is based on one-standard-deviation payoff divided by risk, and uncapped-loss structures are excluded unless allow_uncapped is set. This goes well beyond the annotations.

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 dense and front-loaded with the core function, then layered with usage examples and key behavioral guarantees. No sentence is wasted; each clause earns its place.

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?

For a 7-parameter tool with an output schema, the description covers the essential semantics: inputs, ranking methodology, tradability filtering, and risk defaults. The output schema handles return-value details, so nothing critical is missing.

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

Parameters3/5

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

Schema description coverage is high at 86%, so the schema already documents most parameters. The description reinforces budget sizing and the view concept but adds little parameter-specific meaning beyond what the schema provides, meriting the baseline 3.

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 states a specific verb and resource: given a view and budget, return real option structures priced against the live book. It also gives concrete example queries, making the tool's purpose unmistakable and distinct from siblings like propose_hedge, even without naming them.

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

Usage Guidelines4/5

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

It provides explicit use cases with 'Use this for...' and example natural-language prompts. It doesn't name alternatives or state when not to use it, so it falls short of full exclusion guidance, but the context is clear.

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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