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createMediaSmartCoverJob

Generate intelligent video thumbnails by analyzing content to select optimal cover frames for media files stored in cloud storage.

Instructions

创建媒体智能封面任务

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
objectKeyYes对象在存储桶里的路径

Implementation Reference

  • src/server.ts:448-466 (registration)
    MCP tool registration for 'createMediaSmartCoverJob', including inline schema (objectKey: string), description, and handler function that delegates to CIMediaService.createMediaSmartCoverJob
    server.tool(
      'createMediaSmartCoverJob',
      '创建媒体智能封面任务',
      {
        objectKey: z.string().describe('对象在存储桶里的路径'),
      },
      async ({ objectKey }) => {
        const res = await CIMediaInstance.createMediaSmartCoverJob(objectKey);
        return {
          content: [
            {
              type: 'text',
              text: JSON.stringify(res.data, null, 2),
            },
          ],
          isError: !res.isSuccess,
        };
      },
    );
  • Core handler logic in CIMediaService.createMediaSmartCoverJob: submits SmartCover job to Tencent COS CI via POST /jobs, generates output path, polls status up to 10 times until success or failure.
    async createMediaSmartCoverJob(objectKey: string) {
      try {
        var host = this.bucket + '.ci.' + this.region + '.myqcloud.com/jobs';
        var url = 'https://' + host;
    
        const lastDotIndex = objectKey.lastIndexOf('.');
        const base =
          lastDotIndex === -1 ? objectKey : objectKey.substring(0, lastDotIndex);
    
        const outPutObject = `${base}_\${jobid}_\${number}`;
        var body = COS.util.json2xml({
          Request: {
            Tag: 'SmartCover',
            Input: {
              Object: objectKey, // 存在cos里的路径
            },
    
            Operation: {
              Output: {
                Bucket: this.bucket,
                Region: this.region,
                Object: outPutObject, // 转码后存到cos的路径
              },
              SmartCover: {
                Count: 1,
              },
            },
          },
        });
    
        const createResult = await new Promise((resolve, reject) => {
          this.cos.request(
            {
              Key: 'jobs',
              Method: 'POST', // 固定值
              Url: url,
              Body: body,
              ContentType: 'application/xml',
            },
            (error, data) => (error ? reject(error) : resolve(data)),
          );
        });
        try {
          const jobsDetail = (createResult as any).Response.JobsDetail;
          const initialCode = jobsDetail.Code;
          const initialState = jobsDetail.State;
    
          if (initialCode == 'Failed') {
            return {
              isSuccess: false,
              message: '智能封面任务失败',
              data: createResult,
            };
          }
          if (initialState == 'Success') {
            return {
              isSuccess: true,
              message: '智能封面任务成功',
              data: createResult,
            };
          } else {
            const jobId = jobsDetail.JobId;
    
            // 开始轮询
            let pollResult: any;
            const maxAttempts = 10;
            const interval = 4000;
            for (let attempt = 0; attempt < maxAttempts; attempt++) {
              // 首次立即执行,后续等待间隔
              if (attempt > 0) await new Promise((r) => setTimeout(r, interval));
              try {
                // 查询任务状态
                const { data: getResult } = await this.describeMediaJob(jobId);
                const describeJobsDetail = (getResult as any).Response.JobsDetail;
                const describeJobCode = describeJobsDetail.Code;
                const describeJobState = describeJobsDetail.State;
                // 处理终态
                if (
                  describeJobCode === 'Success' &&
                  describeJobState == 'Success'
                ) {
                  pollResult = getResult;
                  break;
                } else if (describeJobCode === 'Failed') {
                  return {
                    isSuccess: false,
                    message: '智能封面任务失败',
                    data: getResult,
                  };
                }
              } catch (err) {
                // lastError = err as Error; // 记录错误继续重试
              }
            }
    
            if (!pollResult) {
              return {
                isSuccess: false,
                message: `轮询超时(${maxAttempts}次未完成)`,
                data: createResult,
              };
            }
    
            return {
              isSuccess: true,
              message: '智能封面任务成功',
              data: pollResult,
            };
          }
        } catch (error) {
          return {
            isSuccess: false,
            message: '智能封面任务失败',
            data: error,
          };
        }
      } catch (error) {
        return {
          isSuccess: false,
          message: '智能封面任务失败',
          data: 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 states 'create' which implies a write/mutation operation, but it doesn't disclose any behavioral traits like permissions needed, side effects, rate limits, or what the job does (e.g., processing media for covers). This leaves significant gaps in understanding the tool's behavior.

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 a single, concise sentence ('创建媒体智能封面任务') that is front-loaded and wastes no words. However, it's overly brief to the point of under-specification, which slightly reduces its effectiveness despite the efficient structure.

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 a job creation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the job does, what it returns, or any behavioral aspects, making it inadequate for an agent to understand the full context of invoking this tool.

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?

The description adds no meaning beyond what the input schema provides. The schema has 100% description coverage, with 'objectKey' documented as '对象在存储桶里的路径' (path of the object in the storage bucket). Since the schema fully describes the single parameter, the baseline score of 3 is appropriate, as the description doesn't compensate or add extra context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description '创建媒体智能封面任务' (Create media smart cover job) states a clear verb ('create') and resource ('media smart cover job'), but it's vague about what this actually does. It doesn't specify what a 'media smart cover job' entails or what resource it creates, nor does it differentiate from sibling tools like 'createDocToPdfJob' or 'describeMediaJob' beyond the basic resource name.

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, context, or exclusions, such as when to choose this over other job-related tools like 'createDocToPdfJob' or 'describeMediaJob'. There's only a basic statement of purpose 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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