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Glama

AI Skill Store

by io.github.garasegae

validate_compatibility

Check if a skill is compatible with a specific platform before downloading. / 다운로드 전 호환성 검증.
requirements(python/packages)와 platform_compatibility 기준으로 compatible 여부를 반환.

Args:
    skill_id: 검증할 스킬 ID
    python_version: 에이전트 Python 버전 (예: "3.11.2")
    os: "linux" | "darwin" | "windows"
    installed_packages: {"requests": "2.31.0"} 형태 dict (선택)
    target_platform: 설치 대상 플랫폼 ("ClaudeCode" 등)

Returns:
    요약 문자열 (compatible 여부 + 누락 패키지 + 추천 설치 명령)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
osNo
skill_idYes
python_versionNo
target_platformNo
installed_packagesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the return format (summary string with compatibility, missing packages, and install commands) and the criteria (requirements and platform_compatibility). It does not mention potential side effects or error conditions, but as a read-only validation tool, this is adequate.

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 well-structured with a clear purpose sentence, a concise argument list, and a returns section. Though bilingual, the Korean portion directly mirrors the English and does not inflate length. Every line serves a purpose, making it efficient and easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

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, one nested object) and the absence of annotations, the description covers purpose, parameters, return format, and usage context. The explicit returns summary and optional parameter notes provide sufficient detail for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description provides detailed explanations for all five parameters, including example formats and value ranges (e.g., os: 'linux' | 'darwin' | 'windows', installed_packages: {'requests': '2.31.0'}). Since schema_description_coverage is 0%, this fully compensates and adds significant meaning beyond the raw schema.

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 clearly states 'Check if a skill is compatible with a specific platform before downloading', using a specific verb and resource. It differentiates from siblings like download_skill and get_skill by explicitly mentioning the 'before downloading' context and the validation criteria.

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 phrase 'before downloading' implies the tool should be used prior to download_skill, providing clear contextual guidance. However, it does not explicitly state when not to use this tool or mention alternative tools for other scenarios, so it stops short of a full 5.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes, but check_vetting_status and get_vetting_result both deal with security vetting and could be confused. Similarly, get_agent_author_stats and get_agent_identity_stats overlap somewhat, though descriptions differentiate them.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., search_skills, upload_skill_draft, get_vetting_result). No mixed conventions or vague verbs.

Tool Count4/5

18 tools is slightly above the typical well-scoped range (3-15), but the coverage of a skill marketplace (search, browse, upload, vet, review, stats, install) justifies the count. It doesn't feel bloated.

Completeness3/5

Core workflows are covered: search, download, upload, review, and status checks. However, there is no tool to update or delete an uploaded skill, and no way to list skills uploaded by a particular author, which are notable gaps for a marketplace.

Resources