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Vision MCP Server

Vision MCP Server

by Loveacup
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

analyze_image

Analyze an image using a vision language model. Supports local file paths and URLs, with optional prompts for specific questions about the image.

Instructions

Analyze an image using a vision language model. Supports local file paths and URLs.

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": {
      "image": "<image>"
    },
    "name": "analyze_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
imageYesImage source: local file path or URL
detailNoImage detail level for analysisauto
promptNoAnalysis prompt / question about the imageDescribe this image in detail.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations exist. The description mentions support for local paths and URLs but omits details about output format, file size limits, or side effects. It does not specify that the tool returns a text description or answer.

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?

One sentence with essential information, no redundancy. However, could be expanded to include key constraints without becoming verbose.

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 three parameters, no output schema, and no annotations, the description should explain the return format and limitations. It does not specify what the tool returns (e.g., a text description) or any constraints like file format support.

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?

All parameters have schema descriptions (100% coverage). The description adds context for the 'image' parameter by noting local path and URL support, but does not enhance detail or prompt beyond their schema descriptions.

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 verb (Analyze) and resource (image), and specifies support for local file paths and URLs. It differentiates from sibling tools like analyze_video and ocr_image by mentioning vision language model, but does not explicitly contrast with compare_images.

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?

No guidance on when to use this tool versus alternatives (e.g., compare_images, ocr_image). No exclusions or prerequisites provided.

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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