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laukikk

Alpaca Trading MCP Server

by laukikk

place_stop_limit_order

Execute stop-limit orders on Alpaca to automatically buy or sell stocks when a trigger price is reached, with a maximum or minimum execution price.

Instructions

Place a stop-limit order combining stop and limit order features.

Args: symbol: Stock symbol (e.g., 'AAPL') quantity: Number of shares to buy or sell (can be fractional) side: Either 'buy' or 'sell' stop_price: Price that triggers the order limit_price: Maximum/minimum price for the triggered order time_in_force: Order duration - 'day', 'gtc' (good till canceled)

Returns: Order confirmation details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
quantityYes
sideYes
stop_priceYes
limit_priceYes
time_in_forceNoday

Implementation Reference

  • The primary handler function for the 'place_stop_limit_order' tool. It performs input validation using enums, constructs an AlpacaOrderRequest, calls the helper place_order, and formats the response.
    @mcp.tool()
    def place_stop_limit_order(
        symbol: str, 
        quantity: float, 
        side: str, 
        stop_price: float,
        limit_price: float,
        time_in_force: str = "day"
    ) -> str:
        """
        Place a stop-limit order combining stop and limit order features.
        
        Args:
            symbol: Stock symbol (e.g., 'AAPL')
            quantity: Number of shares to buy or sell (can be fractional)
            side: Either 'buy' or 'sell'
            stop_price: Price that triggers the order
            limit_price: Maximum/minimum price for the triggered order
            time_in_force: Order duration - 'day', 'gtc' (good till canceled)
        
        Returns:
            Order confirmation details
        """
        # Validate side
        try:
            order_side = AlpacaOrderSide(side.lower())
        except ValueError:
            return f"Invalid side: {side}. Must be 'buy' or 'sell'."
        
        # Validate time in force
        try:
            order_tif = AlpacaTimeInForce(time_in_force.lower())
        except ValueError:
            return f"Invalid time in force: {time_in_force}. Valid options are: day, gtc"
        
        # Create order request
        order_request = AlpacaOrderRequest(
            symbol=symbol,
            qty=float(quantity),
            side=order_side,
            type=AlpacaOrderType.STOP_LIMIT,
            time_in_force=order_tif,
            stop_price=float(stop_price),
            limit_price=float(limit_price)
        )
        
        try:
            order = calls.place_order(trading_client, order_request)
            
            return (
                f"Stop-limit order placed successfully!\n\n"
                f"Order ID: {order.id}\n"
                f"Symbol: {order.symbol}\n"
                f"Side: {order.side.value}\n"
                f"Type: {order.type.value}\n"
                f"Quantity: {order.qty}\n"
                f"Stop Price: ${order.stop_price:.2f}\n"
                f"Limit Price: ${order.limit_price:.2f}\n"
                f"Time in Force: {order.time_in_force.value}\n"
                f"Status: {order.status.value}\n"
                f"Created At: {order.created_at}\n"
            )
        except Exception as e:
            return f"Error placing stop-limit order: {str(e)}"
  • Pydantic model providing input schema and validation for order requests, supporting stop_limit orders with required stop_price and limit_price fields.
    class AlpacaOrderRequest(BaseModel):
        symbol: str
        qty: Union[int, float]
        side: AlpacaOrderSide
        type: AlpacaOrderType
        time_in_force: AlpacaTimeInForce
        limit_price: Optional[float] = None
        stop_price: Optional[float] = None
        client_order_id: Optional[str] = None
        extended_hours: Optional[bool] = False
  • src/server.py:439-439 (registration)
    The @mcp.tool() decorator registers the place_stop_limit_order function as an MCP tool in the FastMCP server.
    @mcp.tool()
  • Core helper function that translates the validated AlpacaOrderRequest into Alpaca SDK StopLimitOrderRequest and submits it to the trading API.
    def place_order(client: TradingClient, order_details: AlpacaOrderRequest):
        """
        Place an order with flexible order types
        
        :param client: Alpaca trading client
        :param order_details: Order request details
        :return: Placed AlpacaOrder
        """
        # Map Pydantic model to Alpaca order request based on order type
        if order_details.type == AlpacaOrderType.MARKET:
            order_request = MarketOrderRequest(
                symbol=order_details.symbol,
                qty=order_details.qty,
                side=order_details.side,
                time_in_force=order_details.time_in_force
            )
        elif order_details.type == AlpacaOrderType.LIMIT:
            if not order_details.limit_price:
                raise ValueError("Limit price is required for limit orders")
            order_request = LimitOrderRequest(
                symbol=order_details.symbol,
                qty=order_details.qty,
                side=order_details.side,
                time_in_force=order_details.time_in_force,
                limit_price=order_details.limit_price
            )
        elif order_details.type == AlpacaOrderType.STOP:
            if not order_details.stop_price:
                raise ValueError("Stop price is required for stop orders")
            order_request = StopOrderRequest(
                symbol=order_details.symbol,
                qty=order_details.qty,
                side=order_details.side,
                time_in_force=order_details.time_in_force,
                stop_price=order_details.stop_price
            )
        elif order_details.type == AlpacaOrderType.STOP_LIMIT:
            if not (order_details.stop_price and order_details.limit_price):
                raise ValueError("Both stop and limit prices are required for stop-limit orders")
            order_request = StopLimitOrderRequest(
                symbol=order_details.symbol,
                qty=order_details.qty,
                side=order_details.side,
                time_in_force=order_details.time_in_force,
                stop_price=order_details.stop_price,
                limit_price=order_details.limit_price
            )
        else:
            raise ValueError(f"Unsupported order type: {order_details.type}")
    
        # Submit order
        order = client.submit_order(order_request)
        return AlpacaOrder(**order.__dict__)
  • Enum definitions for order side, type (including STOP_LIMIT), and time_in_force used for input validation in the tool handler.
    class AlpacaOrderSide(str, Enum):
        BUY = 'buy'
        SELL = 'sell'
    
    class AlpacaOrderType(str, Enum):
        MARKET = 'market'
        LIMIT = 'limit'
        STOP = 'stop'
        STOP_LIMIT = 'stop_limit'
        TRAILING_STOP = 'trailing_stop'
    
    class AlpacaPositionSide(str, Enum):
        LONG = 'long'
        SHORT = 'short'
    
    class AlpacaTimeInForce(str, Enum):
        DAY = 'day'
        GTC = 'gtc'
        OPG = 'opg'
        CLS = 'cls'
        IOC = 'ioc'
        FOK = 'fok'
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool places an order but doesn't mention critical behaviors like execution risks, confirmation details, error handling, or prerequisites (e.g., account permissions). For a financial trading tool with potential monetary impact, this is a significant gap in 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?

The description is well-structured and front-loaded with the core purpose, followed by organized sections for Args and Returns. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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

Completeness3/5

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

Given the complexity of a financial order tool with no annotations and no output schema, the description covers parameters well but lacks behavioral context (e.g., execution details, errors) and return value specifics. It's adequate for basic usage but incomplete for safe and informed operation in a trading environment.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate fully. It provides clear semantics for all 6 parameters (e.g., 'symbol: Stock symbol', 'stop_price: Price that triggers the order'), adding essential meaning beyond the bare schema. This effectively documents each parameter's purpose and usage.

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 clearly states the specific action ('Place a stop-limit order') and distinguishes it from siblings by mentioning it 'combines stop and limit order features.' This directly differentiates it from place_stop_order and place_limit_order, making the purpose explicit and distinct.

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?

The description implies usage context by explaining it combines features of stop and limit orders, which helps differentiate it from siblings like place_market_order. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., no mention of specific market conditions or strategies), so it doesn't fully reach the highest score.

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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