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Create a character from a photo (paid plans)

create_character_from_photo

Turns a photo of a person (a selfie works best) into a cartoon character: skin, hair, beard and clothes come from the photo, then it can be changed with update_character. Paid plans only, up to 10 photos a day. The photo is only looked at, not stored. Give it as base64 (JPEG, PNG or WebP, up to 3 MB) or an https link. Only use photos of the user, or of people who agreed.

Input Schema

NameRequiredDescriptionDefault
nameYes
projectNo
photo_urlNo
photo_base64No
use_in_videosNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior5/5

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

Annotations only declare the non-read-only, non-idempotent, non-destructive profile; the description adds genuinely new operational facts: plan gating (paid), a rate limit (10/day), a size/format ceiling, and a privacy guarantee ('The photo is only looked at, not stored'). It also names the follow-on tool for modifying the result, which shapes agent planning.

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

Conciseness4/5

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

Front-loaded with the core transformation, then constraints and privacy, with no repeated text from the title. One dense block rather than separated structure, but every sentence carries information.

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?

There is no output schema, and the description never says what a successful call returns (presumably a character, but the identifier needed for the follow-up update_character call is unstated). Prerequisites, limits, and the photo_url vs photo_base64 exclusivity are otherwise well covered for a mutation tool.

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 coverage is 0% across 5 parameters, so the description must carry the load. It explains photo_base64/photo_url well (formats, 3 MB cap, https link, implicit either/or choice) but says nothing about name (1-40 chars), project, or use_in_videos, leaving three parameters undocumented in both places.

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 and resource ('Turns a photo of a person ... into a cartoon character') plus the derived attributes (skin, hair, beard, clothes). It also contrasts with a sibling by noting the result 'can be changed with update_character', and the from-photo scope is unambiguous against create_character.

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?

Gives concrete preconditions: paid plans only, 10 photos/day cap, accepted input as base64 (JPEG/PNG/WebP, up to 3 MB) or an https link, and 'a selfie works best'. It also states the consent boundary ('only use photos of the user, or of people who agreed'). It never explicitly says when to pick this over the plain create_character sibling.

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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