websearch-mcp

image_description

Generate descriptive text for images using vision AI models to analyze visual content and provide detailed textual descriptions.

Instructions

Describe an image using a vision language model.

Args:
    image: Either a base64-encoded string containing the image data,
           or an absolute filesystem path pointing to an image file.

Returns:
    JSON string with description, success status, and error info.

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": "image_description"
  }
}

Input Schema

Name	Required	Description	Default
`image`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Mar 25, 2026

TDQS

A4.3/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. Discloses underlying technology (vision language model) and output format structure (JSON with description, success status, error info). Lacks details on image size limits or failure modes.

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?

Uses structured docstring format with Args and Returns sections. Every sentence provides essential information about inputs, outputs, or behavior. No redundant text.

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

Completeness4/5

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

Appropriate for a single-parameter tool. Covers the input parameter fully and summarizes output schema. Could benefit from noting error conditions or image format restrictions, but sufficient for invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% (no parameter descriptions in schema). Description fully compensates by specifying acceptable formats: 'base64-encoded string' or 'absolute filesystem path', adding critical semantic meaning beyond the bare 'string' type.

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?

States specific action ('Describe'), resource ('image'), and implementation method ('vision language model'). Clearly distinguishes from web-search siblings by domain (images vs. web content).

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

Usage Guidelines3/5

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

No explicit when-to-use versus siblings or alternatives, but usage is implied by the Args section specifying image input requirements (base64 or path). Falls into 'implied usage' category.

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

Deploy Server