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nanoodle

by io.github.nanoodlecom

photo-to-video

Generates a still image from a prompt, then animates it into a short video clip. text:the image being -> text:motion system prompt -> text:movement -> image -> llm -> ivideo; returns video (ltx-2.3-quality) saved to disk (file path in result). Runs on NanoGPT — $0.24 deposit per call, paid in Nano (XNO) — settles at actual model cost + 20%, change returned; no account needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
movementNoText; default: "sailing very fast in rough seas"; 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.
the_image_beingNoText; default: "a tiny sailboat on a glowing bioluminescent sea, night, stars"; optional
motion_system_promptNoText; default: "You are animating this image for ltx-2.3.\nPromptig guide:\n1. Be More Specific. The Engine Can Handle It.\nLTX-2.3 incl..."; optional

TDQS

B3.4/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does disclose important behavior: output is an ltx-2.3-quality video saved to disk, and cost includes a $0.24 deposit with settlement at actual cost + 20% and change returned. It does not mention the two-phase payment flow, but the schema for _payment_id covers that.

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

Conciseness3/5

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

The description is a dense run-on sentence with arrow notation and embedded payment details. It front-loads the purpose but the pipeline string ('text:the image being -> text:motion...') is cryptic and could be structured more clearly.

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

Completeness3/5

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

The description covers the basic return value (video file path) and cost model, which is helpful for a paid tool with no output schema. However, it omits the payment-required response flow and the need to call again with _payment_id after watching; the schema partially compensates, but the description alone is incomplete.

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 the baseline is 3. The description adds value by showing how the_image_being, motion_system_prompt, and movement fit into the pipeline (image generation followed by animation). It does not explain _payment_id, but that parameter's schema is already thorough.

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 clearly states it generates a still image and then animates it into a short video clip, which distinguishes it from static image tools like text-to-image. The pipeline notation adds some confusion, but the core verb+resource is specific.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool vs. siblings like text-to-image, combine-images, or talking-avatar. The usage context is implied by the wording, but there are no decision criteria, prerequisites, or 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