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Kadihx
by Kadihx

GitHub mining & clean-room inspiration

jev_github_mine

Audit any repo's license, detect permissive vs copyleft, and for restricted licenses extract a clean-room architecture spec with 5-gram similarity guard to prove originality.

Instructions

Audit a repository license (permissive vs copyleft/unknown) and, for restricted licenses, extract an architecture-only clean-room spec with a 5-gram similarity guard proving originality.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repoYesowner/name, a GitHub URL, or a local project path.
similarityThresholdNoMax allowed n-gram Jaccard similarity (default 0.15).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It discloses concrete behavior: license classification into permissive/copyleft/unknown and conditional architecture-only extraction with a 5-gram similarity guard. It does not mention side effects or network behavior, but the core behavioral traits are clearly stated.

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 a single dense sentence with no filler. It front-loads the main purpose and then adds the conditional extraction behavior, making it both concise and well-ordered.

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 two-parameter tool with fully documented input schema, the description is nearly complete: it explains the purpose, the conditional behavior, and the output concept. It does not detail output format or failure modes, but the definition is strong enough for an agent to select and invoke the tool correctly.

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 100%, so the schema already documents repo and similarityThreshold well. The description adds limited extra semantic value by connecting the 5-gram similarity guard to the threshold parameter, but it does not substantially expand on the schema's parameter descriptions.

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 states a specific, compound action: it audits a repository license and extracts a clean-room spec for restricted licenses. It uses concrete verbs and a unique resource domain ('GitHub', 'license', 'clean-room spec') that clearly separates it from the sibling tools.

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 description implies its use case: audit a repo license and, when the license is restricted, generate a clean-room spec. However, it does not explicitly name alternatives or state when not to use this tool versus jev_audit, jev_research, or other siblings, so the routing guidance is only implicit.

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