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sartoshi-gen

Generate 1/1 hand-drawn mfer art in the style of sartoshi (CC0) — thin wobbly ink lines, naive doodle style. Fine-tuned by cc0toshi on a public-domain corpus. Output is public domain (CC0). Caption guide at https://cc0.company/skill/sartoshi-gen.md. Skill + prompt templates on GitHub: https://github.com/cryptomfer/cc0company/blob/main/agent-services/sartoshi-gen.md. [PAID: $0.069 USDC per call via x402; async — returns a job_id to poll]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesFree-text prompt describing the scene. Match the sartoshi caption register — short fragment + 'sartoshi signature' closer. See https://cc0.company/skill/sartoshi-gen.md for canonical templates. Max 1000 chars.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / additionalProperties
      Previous value: -trueNew value: +false
    • changedInput schema / properties / prompt / description
      Previous value: -"Input prompt for this service."New value: +"Free-text prompt describing the scene. Match the sartoshi caption register — short fragment + 'sartoshi signature' closer. See https://cc0.company/skill/sartoshi-gen.md for canonical templates. Max 1000 chars."
  2. First observed

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the paid x402 cost ($0.069 USDC/call), the async job_id polling model, and CC0 licensing of the output. It stops short of failure/error behavior or polling cadence, but the key operational traits are surfaced.

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?

Purpose is correctly front-loaded, but the entry is dense: two long URLs plus attribution, licensing, and a bracketed pricing/async block. The caption guide and GitHub links overlap in function, so not every element earns its place.

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?

With no output schema, the description compensates by explaining that the call returns a job_id to poll, and it covers cost and licensing. For a one-parameter generator that is largely complete, though polling/error specifics remain unstated.

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 the schema already documents the single prompt parameter including its char limit and caption-register hint. The description's style framing adds flavor but no syntax or format detail beyond what the schema already provides, so baseline 3 applies.

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?

States a specific verb (Generate) plus resource (1/1 hand-drawn mfer art) and pins the exact style (sartoshi: thin wobbly ink lines, naive doodle). This clearly distinguishes it from the sibling art generators like darkfarms-gen, hokusai-gen, and van-gogh-gen without opening any schema.

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 style description and the caption-register pointer imply when to reach for this tool, but there is no explicit when-to-use/when-not statement and no named alternative among the many sibling generators. Usage is inferable but not spelled out.

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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