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archy_score

Compute a composite quality score for Python projects measuring modularity, acyclicity, depth, and equality. Optionally record results and compare against past runs as a regression gate.

Instructions

Compute the composite quality score (modularity, acyclicity, depth, equality - geometric mean) for a Python project. Optionally append the result to .archy/history.jsonl and/or compare against the most recent recorded run as a regression gate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
internal_onlyNo
recordNo
strictNo
strict_toleranceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
overallYes
componentsYes
inputsYes
gateNo
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses the main behaviors: scoring, optional file appending (record), and optional comparison (strict). However, it does not mention any side effects beyond these, such as whether it modifies the project or requires specific permissions.

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 two sentences, front-loaded with the core purpose, and efficiently covers optional behaviors without unnecessary detail. Every sentence provides value.

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 tool's moderate complexity (5 parameters, optional behaviors) and lack of annotations, the description is largely complete. It explains the score components and optional uses. The existence of an output schema reduces the need to describe return values, though the description could be more explicit about parameter effects.

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 0%, so the description must compensate. It hints at the 'record' and 'strict' parameters by mentioning optional appending and comparison, but does not explain 'path', 'internal_only', or 'strict_tolerance'. The description adds some meaning but not enough to fully cover the five parameters.

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 precisely identifies the tool's action: computing a composite quality score with specific components (modularity, acyclicity, depth, equality) and method (geometric mean). It also distinguishes the tool from siblings like archy_check or archy_cycles by focusing on scoring.

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 mentions optional actions (recording and regression gate comparison) that imply usage scenarios, but lacks explicit guidance on when to use this tool versus alternatives like archy_check. It provides clear context but no when-not advice.

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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