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GPT Image MCP Server

GPT Image MCP Server

by labeveryday
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

analyze_thumbnail

Analyze thumbnail effectiveness for any platform and receive specific improvement suggestions to increase viewer engagement.

Instructions

Analyze a thumbnail's effectiveness and provide improvement suggestions.

Args: image_data: Base64 encoded image data platform: Platform for analysis (youtube, instagram, twitter, facebook, blog) content_category: Content category (e.g., education, entertainment, tech)

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_data": "<image_data>"
    },
    "name": "analyze_thumbnail"
  }
}

Input Schema

NameRequiredDescriptionDefault
platformNoyoutube
image_dataYes
content_categoryNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It mentions analysis and suggestions but doesn't clarify what happens to the image data, whether it's stored, how 'effectiveness' is measured, or any limitations. This leaves significant gaps for an agent deciding whether to invoke the 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 front-loaded with the core purpose, followed by a compact argument list. Every sentence contributes value, and there is no redundant or filler content.

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

Completeness3/5

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

The tool has an output schema, so return values don't need explanation. However, the description omits important context like whether platform and content_category are optional (schema shows platform has a default and content_category can be null), and it doesn't mention any prerequisites or error scenarios. It's minimally viable but not fully complete.

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?

The schema has 0% description coverage, so the description must compensate, and it does. It clearly explains each parameter: image_data as 'Base64 encoded image data', platform with example values, and content_category with examples. This adds meaningful semantics beyond the bare schema, though it could be more precise about format or optionality.

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 function: 'Analyze a thumbnail's effectiveness and provide improvement suggestions.' This uses a specific verb ('analyze') and resource ('thumbnail's effectiveness'), and distinguishes it from sibling tools like generate_image and optimize_for_platform by focusing on evaluation rather than creation or optimization.

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?

The description implies when to use the tool—when you need to assess a thumbnail and receive suggestions—but it doesn't explicitly compare it to alternatives like optimize_for_platform. It provides clear context but no exclusions or direct 'when to use' guidance, so it falls short of the ideal.

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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