deepseek-vision-mcp

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analyze_image

Describe images, screenshots, charts, and photos for text-only LLMs. Switch to OCR mode to extract exact text or table content.

Instructions

当用户提供图片、截图、图表、照片，或要求查看图像内容时，调用本工具获取图片的视觉内容描述。

注意：本工具面向「自身无法直接查看图片」的主模型。若你自身具备原生视觉、能直接查看图片内容，应优先使用自己的原生视觉能力，无需调用本工具；仅在自身无法直接看图时才使用本工具。

默认使用 mode=full 进行完整视觉理解。仅当用户明确要求精确提取图片文字/表格内容时，使用 mode=ocr。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path_or_url": "<path_or_url>"
    },
    "name": "analyze_image"
  }
}

Input Schema

Name	Required	Description	Default
`mode`	No	"full"（默认，全面理解）或 "ocr"（仅提取文字）。	full
`path_or_url`	Yes	本地图片路径或 http(s) URL。

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 1, 2026

TDQS

A4.7/5.0

Behavior4/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It discloses the two operational modes and their behavior (full visual understanding vs. OCR text extraction), which is sufficient for a read-only image analysis tool.

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: trigger conditions, usage caveat about native vision, and mode selection guidance are all front-loaded. No sentence is wasted, and the note about native vision is essential for correct agent decision-making.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the core context: what the tool does, when to invoke it, how to choose modes, and the required parameter. An output schema exists, so the return format does not need explanation. The tool is simple with only two parameters, and the description is complete for correct invocation.

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?

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining when to use mode=ocr versus mode=full, which goes beyond the schema's brief mode description. The path_or_url parameter is already well-covered by the schema.

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

Purpose5/5

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

The description clearly states the tool's purpose: when the user provides an image, screenshot, chart, or photo, call the tool to obtain a visual content description. It uses a specific verb and resource, and the note about native vision distinguishes its intended role from the model's own capabilities.

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 explicitly defines when to use the tool (when the model cannot directly view images) and when not to use it (if the model has native vision). It also provides clear mode selection guidance: use mode=full by default, and mode=ocr only for explicit text/table extraction.

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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analyze_imageA