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phara23

@alpha-arcade/mcp

by phara23

create_limit_order

Place a limit order on an Algorand prediction market with price and quantity in microunits; locks ~0.957 ALGO collateral refundable on cancel or fill.

Instructions

Place a limit order on a prediction market. Price and quantity in microunits (500000 = $0.50, 1000000 = 1 share). Locks ~0.957 ALGO collateral (refunded on cancel/fill). Returns escrowAppId — save it for cancel_order.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
priceYesPrice in microunits (e.g. 500000 = $0.50)
isBuyingYestrue = buy order, false = sell order
positionYes1 = Yes, 0 = No
quantityYesQuantity in microunits (e.g. 1000000 = 1 share)
marketAppIdYesThe market app ID
Behavior4/5

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

Discloses collateral lock amount (~0.957 ALGO, refundable), return value (escrowAppId), and suggests saving it for cancel_order. This provides essential behavioral context beyond a simple 'place order' statement, especially given no 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?

Three tightly written sentences: purpose, unit explanation with collateral info, and return value instruction. No fluff, front-loaded with key 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?

Covers all 5 parameters (via schema), explains units, collateral, and return value. No output schema exists, but description tells the agent what to expect. Minor gaps: no mention of balance requirements or order lifecycle post-placement.

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?

Adds concrete examples for price and quantity (e.g., '500000 = $0.50'), clarifying the microunits concept beyond the schema descriptions. While 100% schema coverage provides baseline, the examples significantly enhance understanding.

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?

Clearly states the action ('Place'), the resource ('limit order on a prediction market'), and includes specific details about units and return value. This distinguishes it from sibling tools like create_market_order or cancel_order.

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

Usage Guidelines3/5

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

The description implies use for limit orders that require collateral and can be cancelled, but does not explicitly compare to alternatives like market orders or provide when-not-to-use scenarios.

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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