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nanoodle

by io.github.nanoodlecom

combine-images

Combine two images into a scene. upload×2 -> text -> edit; returns image (qwen-image-3, 1024×1024) saved to disk (file path in result). Runs on NanoGPT — $0.18 deposit per call, paid in Nano (XNO) — settles at actual model cost + 20%, change returned; no account needed; last run $0.075, ~1m 49s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
TextNodefault: "Make these two people explore a dungeon together"; optional
ImageYes* required; image — file path or https URL
Image_2Yes* required; image — file path or https URL
_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

A4/5.0
Behavior5/5

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 so thoroughly. It discloses the output format (qwen-image-3, 1024×1024), that the result is saved to disk with a file path, the payment model ($0.18 deposit, Nano/XNO, 20% fee, change returned), no-account requirement, and typical runtime/cost from the last run.

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 front-loaded with the core purpose, followed by a compact process/output note, then a dense but relevant cost/runtime clause. Every element earns its place and there is no filler or redundancy.

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?

The tool has async and payment complexity, and the description covers output, cost, and runtime, which is valuable given no output schema. However, it does not explicitly describe the two-phase payment flow (payment-required response then call again with _payment_id), leaving that to the schema's parameter description.

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 description coverage is 100%, so each parameter is already documented. The description's 'upload×2 -> text -> edit' hint loosely maps to Image, Image_2, and Text, but adds no new semantic detail beyond the schema and does not clarify _payment_id beyond what the schema already explains.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Combine two images into a scene,' which uses a specific verb and resource clearly indicating the tool's core function. It distinguishes the operation from siblings like edit-a-photo or text-to-image through the explicit two-image combination, but it doesn't name alternatives or contrast with them.

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 'upload×2 -> text -> edit' flow implies the input pattern and the 'no account needed' note gives some accessibility context. However, it provides no explicit guidance on when to use this tool versus alternatives like edit-a-photo or text-to-image, and no when-not-to-use conditions.

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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