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nanoodle

by io.github.nanoodlecom

talking-avatar

TTS speaks your line, and the lipsync model animates the portrait to match. upload -> text:speak -> text:movement -> tts -> lipsync; returns video (longcat-avatar-1.5) saved to disk (file path in result). Runs on NanoGPT — $0.61 deposit per call, paid in Nano (XNO) — settles at actual model cost + 20%, change returned; no account needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ImageYes* required; image — file path or https URL
speakNoText; default: "Hi there! I used to be a still photo — then somebody wired three nodes together, and now I won't stop talking."; optional
movementNoText; default: "exaggerated head movement"; 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

A4.2/5.0
Behavior4/5

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

With no annotations, the description shoulders the disclosure burden. It reveals the tool's side effects (file saved to disk, path returned in result), the payment mechanism (deposit, settlement, refund), and the lack of account requirement. This is substantial behavioral context for an AI agent to anticipate consequences.

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 a single, dense paragraph that front-loads the purpose, then the workflow, output, and payment. It packs considerable information without redundancy or fluff.

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 the lack of an output schema, the description appropriately explains the return value (video path on disk) and the payment flow. It covers the main user-facing aspects of the tool, though it omits details like duration limits or input constraints.

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?

The input schema already provides detailed descriptions for all four parameters, including defaults and special instructions for _payment_id. The description's pipeline wording ('TTS speaks your line', 'lipsync model animates') loosely aligns with the speak and movement parameters, but it adds no new parameter-level detail beyond the schema.

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 portrait, synthesizes speech from text, and animates the lips to match, producing a video. This distinguishes it from other media tools like sing or photo-to-video by explicitly mentioning the TTS and lipsync pipeline.

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?

The description provides a concrete pipeline ('upload -> text:speak -> text:movement -> tts -> lipsync') that tells the agent the sequence of inputs and how the tool processes them. It also notes that no account is needed, which helps set expectations, but it does not explicitly compare against sibling tools.

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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