Skip to main content
Glama

vision-analyze

Analyze any image URL: describe scenes, extract text, interpret charts, evaluate interfaces, identify objects, or answer questions using GPT-4o-mini vision.

Instructions

Analyze any image URL using GPT-4o-mini vision. Returns structured analysis based on the mode: describe (full description), ocr (text extraction), chart (data/trend extraction), ui (interface analysis), identify (object/subject ID), or qa (answer a specific question about the image). Input must be a publicly accessible image URL (JPEG, PNG, GIF, WebP). $0.050/call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoPublicly accessible URL of the image to analyze. Must return image/jpeg, image/png, image/gif, or image/webp content-type. Max file size: 20MB.
modeNoAnalysis mode: describe (full scene description), ocr (text extraction), chart (data/chart analysis), ui (UI screenshot analysis), identify (object/subject identification), qa (answer a specific question about the image — requires the 'question' parameter).
questionNoFor mode=qa only: the specific question to answer about the image. E.g., 'What is the total revenue shown in Q3?' or 'What does the error message say?'
detailNoOpenAI vision detail level. 'auto' (default): model decides based on image size. 'low': faster, cheaper, less detail (best for simple images). 'high': slower, more detail (best for charts, dense text, complex scenes).
Behavior3/5

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

No annotations exist; description mentions model (GPT-4o-mini), structured analysis, and mode-specific returns, but omits error handling, latency, or privacy considerations.

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?

Two sentences plus bullet-like mode list; front-loaded with essential information, no wasted words.

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?

Covers purpose, constraints, cost, and mode details; lacks explicit output format but sufficient given no output schema.

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?

100% schema coverage; description adds value by explaining each mode's output, giving examples for 'question' parameter, and clarifying 'detail' levels beyond schema descriptions.

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?

Clearly states it analyzes image URLs using GPT-4o-mini vision, lists all six modes with distinct outputs, and differentiates from sibling tools (no other vision analysis tool present).

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

Usage Guidelines4/5

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

Provides input requirements (publicly accessible URL, format, size), cost, and mode options; lacks explicit when-to-use vs alternatives but modes guide task selection.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/thebrierfox/the-stall'

If you have feedback or need assistance with the MCP directory API, please join our Discord server