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Muvon

mcp-binance-futures

by Muvon

get_open_orders

Retrieve active pending orders for a specific trading pair on Binance USDT-M Futures, including regular and conditional orders with detailed status information.

Instructions

Get open orders for a symbol.

Returns list of: orderId, clientOrderId, symbol, status, type, side, positionSide, price, origQty, executedQty, avgPrice, stopPrice, timeInForce, reduceOnly, closePosition, updateTime. Algo (conditional) orders also include '_isAlgo': True.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesTrading pair, e.g. 'BTCUSDT'
sourceNoWhich orders to fetch: 'regular' = standard orders (LIMIT/MARKET), 'algo' = conditional orders (STOP_MARKET/TAKE_PROFIT_MARKET/etc.), 'all' = both merged (default). Algo orders include '_isAlgo': True — pass is_algo=True to cancel_order for those.all

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses the return format comprehensively and mentions special handling for algo orders ('_isAlgo': True), which is valuable behavioral context. However, it doesn't mention rate limits, authentication requirements, error conditions, or whether this is a read-only operation (though 'get' implies reading). The description doesn't contradict any annotations since none exist.

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 efficiently structured with a clear purpose statement followed by return format details. The two sentences earn their place by providing essential information. However, the long list of return fields could have been summarized more concisely, and the algo order note feels slightly tacked on rather than integrated.

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?

Given the tool has an output schema (though not shown here), the description doesn't need to explain return values in detail, yet it provides a comprehensive list anyway. With 100% schema coverage and no annotations, the description adds good value through return format disclosure and algo order handling. For a read operation with good schema documentation, this is reasonably complete, though it could mention authentication or rate limits.

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 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. The baseline of 3 is appropriate when the schema does all the parameter documentation work, though the description could have explained why 'source' parameter matters for the return format.

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 clearly states the action ('Get open orders') and resource ('for a symbol'), making the purpose immediately understandable. It distinguishes from siblings like 'get_order' (single order) and 'get_order_history' (historical orders) by focusing on current open orders. However, it doesn't explicitly contrast with 'cancel_all_orders' or 'modify_order' which also operate on open orders.

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 usage context through the return format details and algo order note, but doesn't explicitly state when to use this tool versus alternatives. No guidance is provided about when to use 'get_open_orders' versus 'get_order' (for specific order) or 'get_order_history' (for completed orders), though the 'open orders' focus provides some implicit differentiation.

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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