Skip to main content
Glama
usensedata

usensedata-mcp-server-query-china-company

Official
by usensedata

query_company_trademark_list

Retrieve trademark details for Chinese companies by providing the full company name. Use this tool to get trademark names, company names, and status information.

Instructions

Obtain the list of an company's trademarks, including trademark names, company names, and statuses, by its full name. Please use the fuzzy query tool to obtain the company full name before calling this tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entNameYescompany full name

Implementation Reference

  • Handler function that takes company full name (entName), prepares request data, calls the yushantwo API helper with product ID 'COM140' specific to trademark list query, and returns the response as text content.
    async ({entName}) => {
      const requestData = {
        entName: entName
      };
      const prodId = "COM140";
      const data = await yushantwo(requestData, prodId);
      return {
        content: [
          {
            type: "text",
            text: data,
          },
        ],
      };
    }
  • Input schema defining the required 'entName' parameter as a string for the company full name.
    {
      entName: z.string().describe("company full name"),
    },
  • src/index.ts:230-251 (registration)
    MCP server tool registration for 'query_company_trademark_list', including name, description, input schema, and inline handler function.
    server.tool(
      "query_company_trademark_list",
      "Obtain the list of an company's trademarks, including trademark names, company names, and statuses, by its full name. Please use the fuzzy query tool to obtain the company full name before calling this tool.",
      {
        entName: z.string().describe("company full name"),
      },
      async ({entName}) => {
        const requestData = {
          entName: entName
        };
        const prodId = "COM140";
        const data = await yushantwo(requestData, prodId);
        return {
          content: [
            {
              type: "text",
              text: data,
            },
          ],
        };
      }
    );
  • Shared helper function yushantwo that handles API calls to Yushan service: encrypts request with AES, sends POST with specific headers, decrypts response. Used by all tools including this one with prodId 'COM140'.
    async function yushantwo(requestData: RequestData, prodId: string): Promise<string> {
      const url = "***";
      const reqTime = Date.now();
      const requestSN = Array.from({ length: 32 }, () => "ABCDEFGHJKMNPQRSTWXYZabcdefhijkmnprstwxyz2345678"[Math.floor(Math.random() * 58)]).join("");
    
      const requestBody = JSON.stringify({
        prod_id: prodId,
        req_data: requestData,
        req_time: reqTime,
        request_sn: requestSN,
      });
      
      // 加密请求数据
      const encryptedRequest = encrypt(requestBody, apiKey);
    
      const headers = {
        AES_KEY: apiKey,
        ACCT_ID: apiUserId,
        ENCODE: "AES256",
      };
    
      try {
        const response = await fetch(url, {
          method: "POST",
          headers: headers,
          body: encryptedRequest,
        });
    
        const responseText = await response.text();
        
        // 解密返回数据
        const decryptedString = decrypt(responseText, apiKey);
        return decryptedString;
      } catch (e) {
        console.error("Error:", e);
        return "error";
      }
    }
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool retrieves trademark data but lacks details on permissions, rate limits, error handling, or response format. For a query tool with no annotations, this leaves significant gaps in understanding its operational behavior.

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 concise and well-structured in two sentences. The first sentence clearly states the purpose, and the second provides essential usage guidance without unnecessary details, making it 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 no annotations and no output schema, the description adequately covers the tool's purpose and usage prerequisites. However, it lacks details on behavioral aspects like response format or error conditions, which are important for a query tool. It meets minimum viability but has clear gaps in completeness.

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%, with the parameter 'entName' documented as 'company full name'. The description adds context by emphasizing the need for the 'full name' and linking it to the fuzzy query tool, but does not provide additional semantic details beyond what the schema already covers, aligning with the baseline score.

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: 'Obtain the list of an company's trademarks, including trademark names, company names, and statuses, by its full name.' It specifies the verb ('obtain'), resource ('trademarks'), and scope ('list'), but does not explicitly differentiate it from sibling tools like query_company_basic_info or query_company_software_copyright_info, which might also retrieve company-related data.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: 'Please use the fuzzy query tool to obtain the company full name before calling this tool.' It names the alternative tool (fuzzy_query_company) and specifies when to use it (to get the correct company name), offering clear prerequisites for effective use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/usensedata/usensedata-mcp-server-query-china-company'

If you have feedback or need assistance with the MCP directory API, please join our Discord server