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nanoodle

by io.github.nanoodlecom

deslop

Paste AI-sounding text. Grok rewrites it like a person wrote it, a second model grades the rewrite as an AI detector would, then Grok gets the draft, the rewrite and the report and does the final p… text:Your draft -> llm×2 -> join×2 -> llm; returns text. Runs on NanoGPT — $0.25 deposit per call, paid in Nano (XNO) — settles at actual model cost + 20%, change returned; no account needed; last run $0.0341, ~78s.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
Your_draftNoText; default: "In today's fast-paced digital landscape, keeping your team in sync has never been more critical. That's why we're thr..."; 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 rewrite AI-sounding text so it reads like a person wrote it. Output only the rewritten text — no preamble, no not..."; optional
System_prompt_2NoSystem prompt; default: "You are a blunt AI-text detector. You see one passage and nothing else. Decide whether a machine wrote it.\nReply in e..."; optional
System_prompt_3NoSystem prompt; default: "You are the final editor. Your input holds three blocks in this order:\n1. the ORIGINAL DRAFT — everything before the ..."; optional

TDQS

B3.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full transparency burden and does disclose important behavioral traits: the orchestration flow ('llm×2 -> join×2 -> llm'), the payment model ($0.25 deposit, 20% fee, Nano), performance stats ('last run $0.0341, ~78s'), and that it returns text. It does not cover failure modes or rate limits, but the payment/watch phases are covered in the schema's _payment_id parameter.

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 single run-on sentence with an apparent truncation ('final p…') followed by a cryptic data-flow diagram ('text:Your draft -> llm×2 -> join×2 -> llm') and then payment details. The structure is poor: the core purpose is buried in the middle, and the truncation is a significant formatting flaw.

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?

This is a complex tool with a multi-model pipeline, payment flow, and no output schema. The description provides the processing chain, cost, and duration, which is helpful, but the truncated final step and cryptic notation leave gaps. The schema's _payment_id parameter fills in the payment-stream behavior, but the description alone is only moderately complete.

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 schema covers 100% of parameters with detailed descriptions, including defaults and the special _payment_id semantics, so the baseline is 3. The tool description adds little param-specific value beyond 'Paste AI-sounding text' which maps to Your_draft; it does not explain the relationships among the System_prompt parameters.

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 conveys that the tool rewrites AI-sounding text into human-sounding text via a multi-model pipeline ('Grok rewrites it... a second model grades... final p…'), and states the return type as text. However, the truncation in 'final p…' and the cryptic 'llm×2 -> join×2 -> llm' notation make the statement less crisp, and no clean verb+resource phrasing is used.

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 opening 'Paste AI-sounding text' implies when to use it, and the description provides practical usage context (payment, no account needed, costs). Yet it does not explicitly mention alternatives or when not to use it, and the sibling tools are mostly media-related, so the tool's text niche is implied rather than stated as a usage guideline.

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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