analyze-image-mcp

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analyze_image

Analyze images and get detailed text descriptions via a separate vision model when the active model cannot see images. Supports local paths, URLs, and data URIs.

Instructions

Analyzes an image using a dedicated vision model and returns a detailed text description. Use this whenever the user attaches or references an image and the active model cannot see images itself. Accepts a local file path, file:// URI, http(s) URL, or data: URL.

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_path": "<image_path>"
    },
    "name": "analyze_image"
  }
}

Input Schema

Name	Required	Description	Default
`question`	No	Optional specific question about the image. If omitted, a full generic description is returned.
`image_path`	Yes	Local path, file:// URI, http(s) URL, or data: URL of the image to analyze.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 29, 2026

TDQS

A4.2/5.0

Behavior3/5

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

With no annotations, the description carries the full disclosure burden. It usefully reveals that analysis is delegated to an external vision model and that output is prose text, but it says nothing about latency, cost, image size/format limits, or failure behavior when a path or URL is unreachable.

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?

Three tight sentences with the core purpose front-loaded, followed by the usage condition and input formats. Every sentence carries distinct information and there is no filler.

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?

With no output schema, the description correctly compensates by stating that a detailed text description is returned, and it covers triggering conditions and accepted input forms. It is close to complete; the remaining gap is operational behavior (limits, cost/latency, error handling) for an external-model call.

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?

Schema description coverage is 100%, so both parameters are already documented. The description's enumeration of accepted input forms (local path, file:// URI, http(s) URL, data: URL) restates the image_path schema text rather than adding new meaning, and it says nothing about the optional 'question' parameter's effect beyond what the schema states. Baseline 3 applies.

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 names a specific verb ('Analyzes'), the resource ('an image'), the mechanism ('a dedicated vision model'), and the result ('a detailed text description'). There are no sibling tools to disambiguate from, so the statement is fully self-contained and unambiguous.

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?

It gives an explicit trigger ('whenever the user attaches or references an image') plus a gating condition that functions as a when-not rule ('the active model cannot see images itself'). No alternatives exist to name, so nothing further is required for correct selection.

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

Deploy Server

Other Tools

analyze_imageA