This server presents 19 tools for Tencent Cloud COS (object storage) and CI (image/media/document processing) operations. While schemas are present and descriptions exist, quality is inconsistent. Naming follows verb_noun patterns well (get*, put*, create*, etc.), but descriptions are often minimal (10-50 chars) and lack context about when to use each tool or what happens next. Parameters have type definitions via Zod, but many lack meaningful descriptions, parameter descriptions are often just field names repeated or minimalist. Output schemas are undocumented; responses are hardcoded to JSON stringification with no clear field contract for agents. Security is a concern: the server masks secrets in responses (maskSecret function) but does not validate that credentials are never exposed as parameters, config appears to be passed server-side which is correct, but getCosConfig returns a stringified config object, leaking SecretId/SecretKey fields (even if masked) into response content. Error handling returns isError flags and JSON responses but lacks guidance for LLM recovery, no categorization of retryable vs. fatal errors. Composition is weak: many tools do similar things (putObject, putString, putBase64, putBuffer) without clear guidance on which to use when. Output pagination and limits are not addressed. Overall, this is a functional but unprofessional toolkit that would benefit from stricter description standards, clearer parameter contracts, documented output schemas, and security hardening.
图片处理-抠图
图片处理-二维码识别-识别存储桶内二维码图片内容
图片处理-超分辨率
图片处理-图片质量评估
文档处理-文档转PDF
媒体处理-视频智能封面
查询存储桶内的文件列表
Output schemas are undocumented for all 19 tools. Agents cannot plan downstream actions or extract fields without understanding the response structure. Tools return stringified JSON with no field contract.
Parameter descriptions are minimal or repetitive. Many descriptions are under 20 characters, simply restate the parameter name, or are in Chinese only without English context. This prevents LLMs from understanding valid inputs and constraints.
Four put* tools (putObject, putString, putBase64, putBuffer) overlap in functionality with no clear guidance on when to use each. LLMs will struggle to pick the right variant. Should either consolidate or add clarifying descriptions.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 50 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 0 | - | v1 |
获取COS配置, 腾讯云配置
下载存储桶内的文件
获取存储桶内的文件的带签名的下载链接
图片处理-获取图片信息
图像搜索-通过图片搜索
图像搜索-通过文本搜索
上传base64编码内容到存储桶
上传buffer内容到存储桶
上传本地文件到存储桶
通过 url下载文件并将文件上传到存储桶
上传字符串内容到存储桶
图片处理-文字水印
Pagination not implemented for list_* tools (getBucket). No limit, offset, or cursor parameters. No guidance on result count limits. Violates pagination pattern; large buckets will exhaust context or fail silently.
Asynchronous tools (createDocToPdfJobs, createMediaSmartCoverJob) do not document how to poll for results or retrieve the job ID. No guidance on how long operations take or how to track progress.
Security: getCosConfig tool returns stringified config object including SecretId and SecretKey (even if masked). Returning any secret-related fields into response content is a leak risk. Tool should not return credentials at all.
Error handling is minimal. Tools return isError flag and JSON stringified data, but no guidance on what to do next (retry? user-fixable? fatal?). No categorization of error types.
Tool descriptions are mostly in Chinese with minimal English context. This limits discoverability and usability for non-Chinese-speaking agents and developers.
Parameter types are sometimes wrong: aiPicMatting uses string for width/height instead of number. This invites type confusion and LLM hallucination.
getObject tool lacks documentation of output format. Agents cannot determine if output is base64, binary, text, or structured JSON.