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nanoodle

by io.github.nanoodlecom

render-a-mockup

Describe a screen, keep the style guide: an LLM turns both into one dense image prompt and an image model renders the mockup. text×2 -> join -> llm -> image; returns image (qwen-image-3) saved to disk (file path in result). Runs on NanoGPT — $0.20 deposit per call, paid in Nano (XNO) — settles at actual model cost + 20%, change returned; no account needed; last run $0.079, ~2m 16s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
TextNodefault: "A habit-tracker web app dashboard: today's checklist, current streaks, a week-at-a-glance grid, and a quick-add button."; optional
Text_2NoText; default: "Style guide: premium, cinematic dark-mode product dashboard with an immediate wow factor, rendered as an exceptionall..."; optional
_payment_idNoPayment id from this tool's previous payment-required response. Phase 3 only: after /x402/watch closes with status paid, call again with the same arguments plus this id to open the RESULTS stream. Do not pass it while payment is still pending — monitor the watch SSE first.
System_promptNoSystem prompt; default: "You write image prompts for UI mockups. Given a screen description and a style guide, reply with ONE detailed image p..."; optional

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden. It discloses key behaviors: qwen-image-3 model, result saved to disk with file path, payment deposit/refund mechanics, no account needed, and approximate runtime/cost. This is unusually transparent for an unannotated tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense run-on sentence that front-loads purpose and packs in pipeline, output, and payment details. It is informative but could be better structured; still, every piece offers value.

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?

Given no output schema and no annotations, the description covers result location, model, cost, and payment flow. It lacks explicit guidance on use cases vs sibling tools, but enough context is present for an agent to understand the tool's behavior and requirements.

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?

Schema coverage is 100%, so baseline is 3. The description adds the conceptual pipeline 'text×2 -> join -> llm -> image', clarifying how Text and Text_2 combine, but it does not detail parameter formats beyond what the schema already provides.

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: it takes a screen description and style guide, an LLM combines them into an image prompt, and an image model renders a mockup. This distinguishes it from sibling image tools by specifying the mockup generation workflow.

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 conveys the intended context (screen description + style guide → mockup) and the pipeline, plus payment/model constraints. However, it does not explicitly state when to prefer this over siblings like text-to-image or edit-a-photo, or provide exclusions.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools target distinct media transformations (text-to-image, text-to-video, text-to-audio), and the overlapping ones (combine-images vs edit-a-photo, text-to-image vs favicon vs render-a-mockup) have clear specialization in their descriptions. An agent can generally tell them apart, though a couple of image-editing and image-generation tools could be confused.

Naming Consistency3/5

Names use hyphens consistently but follow mixed conventions: verb_noun (combine-images, edit-a-photo, render-a-mockup), noun_to_noun (photo-to-video, text-to-image), single words (deslop, favicon, sing), and compound nouns (image-model-arena, talking-avatar). The variety is readable but lacks a uniform pattern.

Tool Count5/5

Ten tools is an ideal size for a creative media server, covering image, video, audio, and text generation without feeling bloated. Each tool earns its place by addressing a distinct type of creative task.

Completeness4/5

The server covers core creative generation workflows across image, video, audio, and text, with both generation and editing capabilities. Minor gaps exist, such as no direct text-to-video without an intermediate image and no generic audio effects, but the major modalities are represented.

Resources