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Analyze Image (Vision Helper)

vision_helper_analyze_image
Read-onlyIdempotent

Analyze images with a vision model to get text descriptions, answer questions, or compare multiple images. Accepts URLs, file paths, data URIs, or base64.

Instructions

Analyze one or more images with a vision-capable model on OpenRouter and return the analysis as text. Use this whenever you need to know what is in an image but you cannot see it yourself.

Default mode is detailed and thorough (high reasoning effort, high-quality model, automatic retry and fallback). Pass quick: true for a fast, cheap answer (quick model, ~1024-token output, minimal reasoning) — e.g. a yes/no, a caption, or an object check.

Accepts an http(s) URL, local file path, file:// URI, data: URI, or raw base64 (PNG, JPEG, WebP, or GIF only); pass an array of up to 5 to compare images (state the comparison in the prompt). Long analyses are truncated at 25000 characters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYesImage to analyze: URL, file path, data URI, raw base64, or an array of these.
modelNoOpenRouter model ID, e.g. 'qwen/qwen3.8-max'. Defaults to OPENROUTER_MODEL, then a built-in default; see vision_helper_list_models for options.
quickNoSet true for a fast, cheap analysis: quick model (OPENROUTER_QUICK_MODEL), ~1024-token output, minimal reasoning. Good for yes/no checks, captions, object checks, or brief comparisons (up to 5 images).
promptNoOptional instruction for the vision model, e.g. 'Transcribe all text in this screenshot'. When omitted, a general detailed description is used.
max_tokensNoMaximum number of tokens for the vision model's answer.
temperatureNoSampling temperature (0-2). Lower is more deterministic.
Behavior5/5

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

The description discloses behavior beyond annotations: high reasoning effort, high-quality model, automatic retry and fallback, quick mode with ~1024-token output, supported image formats, up to 5 images for comparison, and truncation at 25000 characters. These traits are not present in the annotations, which only declare read-only, open-world, idempotent, non-destructive hints. No contradiction.

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 paragraphs, each serving a purpose: purpose/usage, default vs quick mode, and input formats/limits. Front-loaded with the core action and usage. No filler words.

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

Completeness5/5

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

Given six parameters and no output schema, the description covers all essential aspects: input types, model selection (default and quick), prompt customization, token limits, truncation, and retries/fallback. The output type is clarified as text. Sibling tools are checkable via vision_helper_list_models. Nothing critical is missing.

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 input schema provides 100% description coverage for all six parameters, so baseline is 3. The description adds value by specifying image file types (PNG, JPEG, WebP, GIF), clarifying array usage for comparison (state the comparison in the prompt), and explaining quick mode behavior. This goes beyond the schema's generic 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?

The description opens with a clear verb-resource pair: 'Analyze one or more images with a vision-capable model on OpenRouter and return the analysis as text.' It distinguishes itself from siblings (list_models, check_config) by the task type and explicitly states when to use it: 'whenever you need to know what is in an image.'

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?

It provides clear usage context: 'Use this whenever you need to know what is in an image but you cannot see it yourself.' It also offers conditional guidance for quick mode vs default mode, specifying examples like yes/no, captions, or object checks. It doesn't explicitly mention sibling alternatives or when not to use, but the context is sufficient.

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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