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cfrs2005

GS Robot MCP Server

by cfrs2005

list_robots

Retrieve a paginated list of robots from the Gausium OpenAPI, with optional filtering by relation type.

Instructions

Fetches the list of robots from the Gausium OpenAPI.

Based on: https://developer.gs-robot.com/zh_CN/Robot%20Information%20Service/List%20Robots

Args:
    page: The page number to retrieve (must be > 0).
    page_size: The number of items per page.
    relation: Optional relation type (e.g., 'contract').

Returns:
    A dictionary containing the robot list data from the API.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
page_sizeNo
relationNo

Implementation Reference

  • MCP tool handler for 'list_robots', registered via @mcp.tool() decorator. Executes the tool logic by calling the underlying mcp.list_robots method.
    @mcp.tool()
    async def list_robots(page: int = 1, page_size: int = 10, relation: str = None):
        """Fetches the list of robots from the Gausium OpenAPI.
        
        Based on: https://developer.gs-robot.com/zh_CN/Robot%20Information%20Service/List%20Robots
    
        Args:
            page: The page number to retrieve (must be > 0).
            page_size: The number of items per page.
            relation: Optional relation type (e.g., 'contract').
    
        Returns:
            A dictionary containing the robot list data from the API.
        """
        return await mcp.list_robots(page=page, page_size=page_size, relation=relation)
  • Core helper method in GausiumMCP class implementing the logic to list robots by calling the Gausium API endpoint via GausiumAPIClient.
    async def list_robots(
        self,
        page: int = 1,
        page_size: int = 10,
        relation: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        获取机器人列表。
    
        Args:
            page: 页码 (必须 > 0)
            page_size: 每页数量
            relation: 可选关系类型 (例如 'contract')
    
        Returns:
            包含机器人列表数据的字典
    
        Raises:
            httpx.HTTPStatusError: API调用返回错误状态码
            httpx.RequestError: 网络连接问题
        """
        query_params = {
            "page": page,
            "pageSize": page_size,
        }
        if relation is not None:
            query_params["relation"] = relation
    
        async with GausiumAPIClient() as client:
            return await client.call_endpoint(
                'list_robots',
                query_params=query_params
            )
  • API endpoint configuration definition for 'list_robots', specifying path, method, version, used by the API client for HTTP requests.
    'list_robots': APIEndpoint(
        name="list_robots",
        path="robots",
        method=HTTPMethod.GET,
        version=APIVersion.V1_ALPHA1,
        description="列出机器人"
    ),
  • The @mcp.tool() decorator registers the list_robots function as an MCP tool.
    @mcp.tool()
Behavior3/5

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 mentions pagination behavior (page and page_size parameters) and an optional relation filter, which adds useful context. However, it doesn't disclose authentication requirements, rate limits, error conditions, or what happens when parameters are invalid—significant gaps for an API call tool.

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 well-structured with clear sections for purpose, arguments, and returns. Each sentence adds value, and there's no redundant information. It could be slightly more concise by integrating the URL reference more smoothly, but overall it's efficient and front-loaded.

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 tool's moderate complexity (3 parameters, no output schema, no annotations), the description is partially complete. It covers parameters well but lacks details on authentication, error handling, response format beyond 'dictionary', and when to use versus siblings. For a list operation with pagination, more behavioral context would be helpful.

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?

The description adds meaningful semantics for all three parameters beyond the schema (which has 0% description coverage). It explains that 'page' must be > 0, 'page_size' controls items per page, and 'relation' is an optional type like 'contract'. This compensates well for the schema's lack of descriptions, though it doesn't provide examples or constraints for 'relation' values.

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 the verb 'fetches' and resource 'list of robots from the Gausium OpenAPI', making it easy to understand what the tool does. It distinguishes itself from siblings by focusing on listing robots rather than status checks, commands, or other robot-related operations, though it doesn't explicitly compare to alternatives.

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 like 'get_robot_status_smart' or 'list_robot_commands'. It mentions a source URL but doesn't explain the context or prerequisites for calling this API, leaving the agent without usage context.

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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