Does this tool use last trades when quotes are missing?

No. Derived pricing requires coherent bid and ask data for every leg. Last trades and daily closes are not used as substitutes.

Can it return naked options?

No. Every candidate must satisfy the fixed defined-risk structure and bounded leg-count contract.

Can AI predict stock prices with this MCP tool?

No. AI cannot predict stock prices with certainty, and this MCP tool does not promise future returns. It gives an agent bounded data or validation results that still require independent review, risk controls, and source checking.

Agent workflows

Options Strategy MCP Tool for AI Agents

suggest-options-strategy returns one primary and up to two alternative defined-risk structures for one exact active US stock, ADR, or ETF. It supports four fixed outlooks, three fixed horizons, and two risk styles; derived pricing stays null unless every leg has coherent bid and ask data from the same freshness context.

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MCP tool name

suggest-options-strategy

Example prompt

Use Pineify suggest-options-strategy for a balanced bullish 1m AAPL scenario and explain every coverage gap.

Direct answer

What suggest-options-strategy does

The workflow maps 2w, 1m, and 2m to fixed 14–30, 30–60, and 60–90 calendar-DTE windows. It excludes shorter expiries and invalid chain rows before deterministic contract selection.

Candidates are educational scenarios, not orders. The workflow does not return naked legs, quantities, routing instructions, or last-trade substitutes for missing quotes.

Contract

What the agent sends and receives

Inputs

`symbol`: one exact active US stock, ADR, or ETF ticker.
`outlook`: bullish, bearish, neutral, or large-move.
`horizon`: 2w, 1m, or 2m; `riskStyle`: conservative or balanced; optional `detail`: compact or standard.

Structured output

`options-strategy-v1` structured content and versioned methodology.
One to three candidates, each with two to four ordered legs and explicit defined-risk status.
Coverage, source times, warnings, assumptions, and nullable quote-derived pricing.

Capabilities

Where this MCP tool fits

Select fixed structures

Maps bullish, bearish, neutral, and large-move outlooks to bounded bull call, bear put, iron condor, or long straddle structures.

Preserve quote integrity

Calculates premium, break-even, max profit, and max loss only when all required bid/ask sides are coherent.

Expose event and flow context

Keeps event risk and optional options-flow evidence separate from the chain-based candidate ranking.

Agent workflow

A bounded call from question to review

1
Choose a bounded view
Provide one symbol, a fixed outlook, horizon, and risk style.
2
Inspect structure before pricing
Confirm expiry, legs, Greeks, open interest, and defined-risk invariants.
3
Treat null pricing as evidence
Keep missing coherent quotes visible instead of filling from trades or closes.

Prompt examples

Questions an agent can route to this tool

"Use Pineify suggest-options-strategy for a balanced bullish 1m AAPL scenario and explain every coverage gap."

"Build a conservative neutral 2m SPY options scenario and keep all unavailable pricing fields null."

Operator notes

How I review the result

I check the candidate structure and leg order before interpreting any derived payoff field.

I never turn the educational scenario into an order, position size, or personalized recommendation.

Boundaries to keep in the prompt

Only fixed outlooks, horizons, risk styles, and four structures are supported; arbitrary payoff code is rejected.
Missing or inconsistent bid/ask data makes derived pricing partial or null even when a valid structure exists.
The result is informational, does not predict returns, and does not place or route trades.

Pineify MCP is an information and code-validation tool, not investment advice. It does not promise returns, place trades, or modify a portfolio. Review timestamps, source fields, code diagnostics, and risk assumptions before acting.

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