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nanoodle

by io.github.nanoodlecom

image-model-arena

Pick a DIFFERENT model on each Contender, then Run — same prompt four ways, side by side. text -> image:Contender 1 -> image:Contender 2 -> image:Contender 3 -> image:Contender 4; returns image (gpt-image-2, 1024×1024) + image (nano-banana-2-fast, 1024×1024) + image (qwen-image-3, 1024×1024) + image (krea-v2-large/text-to-image, 1:1) saved to disk (file paths in result). Runs on NanoGPT — $0.59 deposit per call, paid in Nano (XNO) — settles at actual model cost + 20%, change returned; no account needed; last run $0.2455, ~1m 39s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
TextNodefault: "a tiny dragon curled up asleep in a teacup, macro photo, soft morning light"; 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.

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it discloses pricing ($0.59 deposit, actual cost +20%), payment method (Nano/XNO), no account needed, runtime (~1m 39s), and that images are saved to disk with file paths. It also mentions the settlement process. It lacks details on failure modes or the payment flow phases, but given zero annotations, this is comprehensive.

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

Conciseness2/5

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

The description is a long, run-on paragraph that packs excessive details (exact model names, resolutions, pricing, last run stats) into a single block. While informative, the structure lacks conciseness and is not front-loaded beyond the first phrase. Some information (e.g., last run cost and time) is non-essential and could be omitted or moved to a separate field.

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?

Despite having no output schema and no annotations, the description is remarkably complete: it specifies the input prompt, the four models with their exact resolutions, the output format (file paths), the payment and cost model, and approximate runtime. An agent has enough context to set expectations and invoke the tool correctly.

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 coverage is 100% with both Text and _payment_id having clear descriptions. The tool description adds no additional parameter meaning beyond what the schema already states. Baseline 3 applies because the schema does the heavy lifting.

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: pick a different model on each Contender and run the same prompt four ways side by side. It explicitly names the four models and their output sizes, distinguishing it from siblings like text-to-image (which presumably generates one image) and combine-images (which composites existing images).

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 usage context is implied by describing the side-by-side comparison, but there is no explicit statement of when to use this tool versus alternatives like text-to-image. No alternatives are mentioned, and no exclusions are provided. The description implies 'use this to compare models on the same prompt' without directly saying so.

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.

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