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bholzer

tastytrade-mcp

by bholzer

Create order

create_order

Create and preview multi-leg orders for equities, options, futures, or crypto. Use dry-run to check fees and buying-power effect before submitting.

Instructions

Create an order with 1-4 legs (equity, options, futures, crypto). By default this is a DRY RUN that returns the order preview with buying-power effect, fees, and warnings WITHOUT placing it. Set dry_run=false to actually submit the order. Always dry-run first and confirm with the user before submitting a live order.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_numberYesThe account number
legsYesOrder legs
order_typeNoOrder type (default Limit)
time_in_forceNoTime in force (default Day)
priceNoLimit price as a decimal string, e.g. '1.50'. Required for Limit and Stop Limit orders. For multi-leg orders this is the net price
price_effectNoWhether the price is paid (Debit) or received (Credit). Inferred for single-leg orders; required for multi-leg limit orders
stop_triggerNoStop trigger price. Required for Stop and Stop Limit orders
gtc_dateNoExpiration date YYYY-MM-DD, required when time_in_force is GTD
valueNoNotional dollar amount for Notional Market orders, e.g. '10.00'
value_effectNoValue effect for Notional Market orders
dry_runNoIf true (the default), preview the order without submitting it
Behavior4/5

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

No annotations are provided, so the description must carry the full burden. It discloses the dry-run default, the leg count constraint, and that the preview returns buying-power effect, fees, and warnings. However, it does not describe the return value for a live order or address authentication/rate limits, which slightly limits transparency.

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?

Three sentences efficiently convey purpose, default behavior, and usage advice. No superfluous information.

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

Completeness4/5

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

With 11 parameters and no output schema, the description covers the core workflow (dry-run vs. live) and leg constraints. It mentions the preview output, but lacks detail on the live order response or error handling. Still, it provides sufficient context for an agent to use the tool safely.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the dry_run parameter's role and the types of legs. It also notes OCC format for equity options, which goes beyond the schema. This extra context justifies a score above baseline.

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?

Description explicitly states the tool's function: 'Create an order with 1-4 legs', covering multiple instrument types. This clearly distinguishes it from sibling tools like cancel_order or list_orders.

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?

Description provides clear guidance: default behavior is a dry run, and users must set dry_run=false to actually submit. It instructs to always dry-run first and confirm with the user, which is excellent usage guidance.

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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