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Make a local ID photo and print sheet

abox_id_photo

Creates custom ID photos by applying numeric rules and explicit crown, chin, and eye landmarks from a local JSON settings file, writing a JPEG, print sheet, and measurement report to a new directory.

Instructions

Apply custom numeric photo rules and explicit crown, chin and eye landmarks from a local JSON settings file. Writes a JPEG photo, print sheet and measurement report into a new directory. No face detection, country rules or official approval. Checks geometry, DPI, output dimensions and lossy JPEG pixel error. Requires a local browser.

Input Schema

NameRequiredDescriptionDefault
input_pathYesAbsolute local filesystem path. URLs and network shares are refused.
output_dirYesAbsolute local filesystem path. URLs and network shares are refused.
settings_pathYesAbsolute local filesystem path. URLs and network shares are refused.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.9.0

TDQS

A4.2/5.0
Behavior5/5

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

Goes well beyond annotations: discloses that it writes a JPEG photo, print sheet and measurement report into a new directory; performs geometry, DPI, output dimension and lossy JPEG pixel error checks; and requires a local browser. These details clarify side effects and constraints consistent with readOnlyHint=false and destructiveHint=false.

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?

Four sentences, front-loaded with the core action, then outputs, exclusions, checks and the local-browser requirement. Every sentence carries useful information with no repetition or filler.

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?

For a mutation tool with no output schema, the description adequately covers written artifacts, validation checks and runtime requirements. It does not describe the expected JSON settings format or failure behavior, but those are reasonable external details for a three-path-parameter 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 description coverage is 100%, so all three path parameters are already documented in the schema, including the rejection of URLs and network shares. The description mentions the roles of input, settings and output artifacts but adds no syntax or parameter-level detail beyond the schema, so baseline 3 is appropriate.

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 ('Apply custom numeric photo rules') and resource ('ID photo', 'print sheet'), distinguishes the tool from generic image siblings by clarifying 'No face detection, country rules or official approval', and names the exact artifacts written. An agent can tell what this tool uniquely does.

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?

Usage context is implied by 'custom numeric photo rules and explicit crown, chin and eye landmarks from a local JSON settings file', but no sibling alternatives are named and there is no explicit when-to-use vs when-not-to-use guidance beyond the 'no face detection' exclusion. An agent must infer that other image tools are not appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.