AI Skill Store

upload_skill

Upload a skill package to AI Skill Store. Requires an API key. / 스킬 업로드 (API 키 필요).

※ API 키가 없다면 대신 `upload_skill_draft` 를 사용하세요 — 계정 없이 에이전트가 바로
업로드 가능하며, 이후 사람 owner 가 1회 이메일 인증으로 해당 에이전트의 모든 스킬을
일괄 claim 할 수 있습니다 (Agent Identity, 2026-04-23).

**사용 방식 A — JSON content 모드 (에이전트 권장, 디스크 불필요)**:
  - skill_md (필수): SKILL.md 전체 내용 문자열
  - files (선택): {파일명: 파일내용} 딕셔너리. 예: {"main.py": "import sys\n..."}
  - requirements (선택): requirements.txt 내용 문자열
  - author_agent (선택): {"name": "...", "provider": "..."} 또는 그냥 name 문자열

**사용 방식 B — 파일 경로 모드 (기존 호환)**:
  - file_path: 업로드할 .skill 파일의 절대 경로

둘 중 하나만 제공. 둘 다 있으면 JSON content 모드 우선.

Args:
    api_key: 개발자 API 키 (필수). 없으면 upload_skill_draft 를 사용할 것.
    file_path: (방식 B) .skill 파일 경로
    skill_md: (방식 A) SKILL.md 내용
    files: (방식 A) {파일명: 텍스트내용}
    requirements: (방식 A) requirements.txt 내용
    author_agent: (방식 A) 에이전트 attribution

Returns:
    업로드 결과 메시지 (version_id, vetting_job_id, poll_url 포함)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`files`	No
`api_key`	Yes
`skill_md`	No
`file_path`	No
`author_agent`	No
`requirements`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.7/5.0

Behavior4/5

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

With no annotations, the description carries the transparency burden. It discloses the API-key requirement, the two upload modes with priority behavior, and the return fields (version_id, vetting_job_id, poll_url). It does not discuss error conditions or side effects, but this is strong coverage for the described 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?

It is front-loaded and logically organized into mode sections and an Args list. Some redundancy exists because the Args list repeats information already given in the mode descriptions, and there is bilingual duplication, but this is acceptable given the tool's complexity.

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?

The tool has 6 parameters, conditional modes, and a required API key; the description covers all of these, explains return values, and references the relevant fallback sibling. It is complete 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.

Parameters5/5

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

Schema description coverage is 0%, so the description fully compensates. Every parameter is explained—api_key is marked required, skill_md and file_path are described per mode, files/author_agent get type examples, and requirements is clearly defined.

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?

Opening sentence 'Upload a skill package to AI Skill Store' states a specific verb, resource, and scope. It also distinguishes itself from sibling 'upload_skill_draft' by explicitly noting the API-key requirement and naming the alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use guidance: use upload_skill_draft if you lack an API key. It also details two mutually exclusive usage modes, states that only one should be provided, and specifies that JSON content mode takes priority if both are supplied.

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?