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cfrs2005

GS Robot MCP Server

by cfrs2005

generate_task_report_png

Create a PNG map visualization for robot task reports to document cleaning routes and performance data for analysis.

Instructions

Generates a PNG map for M-line task report.

Args:
    serial_number: The serial number of the target robot.
    report_id: The ID of the task report.

Returns:
    A dictionary containing the map generation result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
serial_numberYes
report_idYes

Implementation Reference

  • Core handler implementation for the generate_task_report_png MCP tool. Calls the Gausium API to generate the PNG report map.
    async def generate_task_report_png(
        self,
        serial_number: str,
        report_id: str
    ) -> Dict[str, Any]:
        """
        M线任务报告地图生成。
    
        Args:
            serial_number: 机器人序列号
            report_id: 报告ID
    
        Returns:
            地图生成结果
    
        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 report_id:
            raise ValueError("Report ID cannot be empty")
    
        async with GausiumAPIClient() as client:
            return await client.call_endpoint(
                'generate_task_report_png',
                path_params={
                    'serial_number': serial_number,
                    'report_id': report_id
                }
            )
  • MCP tool registration using @mcp.tool() decorator. Defines the tool schema via parameters and docstring, delegates to the core handler.
    async def generate_task_report_png(serial_number: str, report_id: str):
        """Generates a PNG map for M-line task report.
    
        Args:
            serial_number: The serial number of the target robot.
            report_id: The ID of the task report.
    
        Returns:
            A dictionary containing the map generation result.
        """
        return await mcp.generate_task_report_png(serial_number=serial_number, report_id=report_id)
  • API endpoint configuration used by the GausiumAPIClient in the handler to call the correct path and method.
    'generate_task_report_png': APIEndpoint(
        name="generate_task_report_png",
        path="robots/{serial_number}/taskReports/{report_id}/map",
        method=HTTPMethod.GET,
        version=APIVersion.V1_ALPHA1,
        description="M线任务报告地图生成"
    ),
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states the tool 'generates' a PNG map, implying a read-only or computational operation, but lacks details on permissions, rate limits, whether it creates new files or retrieves existing ones, or error conditions. The return format is vaguely described as 'a dictionary', offering minimal behavioral insight.

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 front-loaded, starting with the core purpose. The Args/Returns structure is clear, though slightly verbose for a simple tool. Every sentence contributes, but the return statement could be more specific (e.g., 'dictionary with keys like 'map_url' or 'error'').

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 no annotations, no output schema, and 0% schema coverage, the description is incomplete. It covers basic purpose and parameters but lacks crucial details: behavioral traits (e.g., side effects, auth), return value specifics, and usage context relative to siblings. For a tool with two parameters and potential complexity, this is inadequate.

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 description must compensate. It lists both parameters ('serial_number' and 'report_id') with brief explanations, adding meaning beyond the bare schema. However, it doesn't specify format constraints (e.g., ID patterns) or dependencies between parameters, leaving gaps in semantic 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 verb 'generates' and the resource 'PNG map for M-line task report', making the purpose understandable. It distinguishes from siblings like 'get_task_reports_smart' by focusing on map generation rather than report retrieval, though it doesn't explicitly compare to similar tools like 'download_robot_map_v1/v2'.

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 an existing report), exclusions, or comparisons to sibling tools like 'download_robot_map_v1/v2' or 'get_map_subareas', leaving usage context unclear.

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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