AI Skill Store

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

Name	Required	Description	Default
`os`	No
`skill_id`	Yes
`python_version`	No
`target_platform`	No
`installed_packages`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries full burden. It explains the validation criteria (requirements, platform compatibility) and the return summary (compatibility, missing packages, install command), giving a good sense of behavior. It does not explicitly state whether the operation is read-only or if any side effects occur, but 'check' implies a safe operation.

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?

The description is well-structured with a short introductory sentence, a concise args list, and a returns section. It is not excessively long, though the bilingual text adds a slight redundancy without hurting clarity.

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?

The description covers the tool's purpose, all parameters, and the return format. Given the tool's moderate complexity (5 params, nested object), it provides enough context for an agent to decide when to invoke it. It does not need to explain the output schema in detail since the description already summarizes the return value.

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

Parameters4/5

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

Schema descriptions are completely absent (0% coverage), so the description must compensate. It does so by listing all five parameters with brief explanations and examples, such as python_version format and installed_packages structure. However, it does not mention default values for parameters that have them in the 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 the tool checks compatibility of a skill with a specific platform before downloading, using a specific verb and resource. This distinguishes it from sibling tools like download_skill or get_install_guide.

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 provides clear usage context with 'before downloading', implying when to invoke the tool. However, it does not explicitly mention when not to use it or point to alternative tools.

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

All AI Skill Store tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0

Disambiguation3/5

Several tool pairs overlap in purpose: check_vetting_status vs get_vetting_result both query security vetting, and get_agent_author_stats vs get_agent_identity_stats both provide agent statistics. Though descriptions are detailed and clarify differences, the similar names and overlapping functionality create potential selection confusion.

Naming Consistency4/5

Tool names follow a consistent lowercase snake_case verb_noun pattern (e.g., search_skills, upload_skill, get_skill). Minor inconsistency exists between check_* and get_* for related status operations, but overall convention is clear.

Tool Count4/5

With 18 tools, the server is moderately comprehensive, covering search, upload, download, vetting, reviews, and platform compatibility. This is slightly above the typical 3-15 well-scoped range but not excessive given the store's multi-faceted domain.

Completeness3/5

Core lifecycle is mostly covered: registration, upload, search, retrieval, download, review, and vetting. However, there are notable gaps—no update or delete operations for uploaded skills, and no method to fetch existing reviews, only post them.

Resources

Need Help?