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cfrs2005

GS Robot MCP Server

by cfrs2005

get_robot_command

Retrieve execution results for specific robot commands by providing the robot's serial number and command ID to monitor task completion and status.

Instructions

Gets the result of a specific robot command.

Args:
    serial_number: The serial number of the target robot.
    command_id: The ID of the command to query.

Returns:
    A dictionary containing the command execution result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
serial_numberYes
command_idYes

Implementation Reference

  • Core handler implementation in GausiumMCP class that performs the actual API call to retrieve the robot command result using GausiumAPIClient.
    async def get_robot_command(
        self, 
        serial_number: str, 
        command_id: str
    ) -> Dict[str, Any]:
        """
        获取单条指令结果。
    
        Args:
            serial_number: 机器人序列号
            command_id: 指令ID
    
        Returns:
            指令执行结果
    
        Raises:
            ValueError: 参数为空
            httpx.HTTPStatusError: API调用错误
            httpx.RequestError: 网络问题
        """
        if not serial_number:
            raise ValueError("Serial number cannot be empty")
        if not command_id:
            raise ValueError("Command ID cannot be empty")
    
        async with GausiumAPIClient() as client:
            return await client.call_endpoint(
                'get_command',
                path_params={
                    'serial_number': serial_number,
                    'command_id': command_id
                }
            )
  • MCP tool registration using @mcp.tool() decorator. This is the entry point handler for the 'get_robot_command' tool, delegating to the core MCP implementation.
    @mcp.tool()
    async def get_robot_command(serial_number: str, command_id: str):
        """Gets the result of a specific robot command.
    
        Args:
            serial_number: The serial number of the target robot.
            command_id: The ID of the command to query.
    
        Returns:
            A dictionary containing the command execution result.
        """
        return await mcp.get_robot_command(serial_number=serial_number, command_id=command_id)
  • Data-driven API endpoint schema/configuration for 'get_robot_command', defining path, method, version, used by GausiumAPIClient to construct HTTP requests.
    'get_command': APIEndpoint(
        name="get_robot_command",
        path="robots/{serial_number}/commands/{command_id}",
        method=HTTPMethod.GET,
        version=APIVersion.V1_ALPHA1,
        description="获取单条指令结果"
    ),
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 this is a read operation ('Gets'), which is helpful, but lacks critical details: it doesn't specify whether this requires authentication, rate limits, error conditions (e.g., invalid command ID), or what happens if the command is still executing. The return format is vaguely described as 'a dictionary', offering minimal insight into structure or content.

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 appropriately sized and well-structured with clear sections for Args and Returns. The purpose is front-loaded in the first sentence, and each subsequent sentence adds value without redundancy. However, the 'Returns' section could be more informative to improve efficiency.

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

Completeness2/5

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

Given the complexity of querying robot commands (likely involving async operations or status checks), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It fails to address key contextual aspects like error handling, command state dependencies (e.g., only works for completed commands), or example return structures, leaving significant gaps for an AI agent.

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 0%, so the schema provides no parameter descriptions. The description adds basic semantics by explaining that 'serial_number' identifies the target robot and 'command_id' specifies the command to query. However, it doesn't elaborate on format (e.g., UUID for command_id), validation rules, or examples, leaving gaps in understanding.

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 tool's purpose with 'Gets the result of a specific robot command', specifying both the verb ('Gets') and resource ('robot command result'). It distinguishes from siblings like 'list_robot_commands' (which lists commands) and 'create_robot_command' (which creates commands), though it doesn't explicitly mention these distinctions in the description text itself.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a command ID from 'create_robot_command' or 'list_robot_commands'), nor does it differentiate from similar tools like 'get_robot_status_smart' or 'list_robot_commands'. Usage is implied only through the purpose statement.

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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