Skip to main content
Glama

validate_compatibility

Check skill compatibility with your platform before downloading. Evaluates Python version, OS, and installed packages, then returns compatibility status, missing dependencies, and installation commands.

Instructions

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
skill_idYes
python_versionNo
osNo
installed_packagesNo
target_platformNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Tool registered with @mcp.tool() decorator on line 954
    @mcp.tool()
    @_log_tool
  • The validate_compatibility handler function: accepts skill_id, python_version, os, installed_packages, target_platform; POSTs to /v1/skills/{skill_id}/validate and formats compatibility result with checks, missing/mismatched packages, suggested install commands, and warnings.
    def validate_compatibility(
        skill_id: str,
        python_version: str = "",
        os: str = "",
        installed_packages: dict = None,
        target_platform: str = "",
    ) -> str:
        """
        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)
        """
        import requests as _rq
        if installed_packages is None:
            installed_packages = {}
        payload = {}
        if python_version: payload['python_version'] = python_version
        if os: payload['os'] = os
        if installed_packages: payload['installed_packages'] = installed_packages
        if target_platform: payload['target_platform'] = target_platform
    
        url = f"{SKILL_STORE_URL}/v1/skills/{skill_id}/validate"
        try:
            r = _rq.post(url, json=payload, timeout=15)
        except Exception as e:
            return f"❌ 요청 실패: {e}"
    
        if r.status_code == 404:
            return "❌ 스킬을 찾을 수 없음"
        if r.status_code != 200:
            try: err = r.json().get('detail') or r.json().get('message') or r.text[:200]
            except Exception: err = r.text[:200]
            return f"❌ 검증 실패 ({r.status_code}): {err}"
    
        d = r.json()
        compat = "✅ 호환" if d.get('compatible') else "❌ 비호환"
        lines = [f"{compat}  {d.get('skill_name')} v{d.get('version')}"]
        for check in d.get('checks', []):
            nm = check.get('name')
            status = check.get('status')
            icon = {'ok':'✅','not_specified':'⚪','informational':'ℹ️','unknown':'⚪','mismatch':'❌','missing':'❌','unsupported':'❌'}.get(status, '•')
            if nm == 'packages':
                miss = check.get('missing') or []
                vmm = check.get('version_mismatch') or []
                sat = check.get('satisfied') or []
                lines.append(f"  {icon} packages: satisfied={len(sat)}, missing={len(miss)}, mismatch={len(vmm)}")
                for m in miss[:5]:
                    lines.append(f"      - missing: {m['name']} {m.get('required','')}")
                for vm in vmm[:5]:
                    lines.append(f"      - mismatch: {vm['name']} req={vm['required']} installed={vm['installed']}")
            else:
                lines.append(f"  {icon} {nm}: {status}  ({check.get('message','')[:80]})")
        sugg = d.get('suggested_install_commands') or []
        if sugg:
            lines.append("  추천 설치:")
            for s in sugg[:8]:
                lines.append(f"    $ {s}")
        warns = d.get('warnings') or []
        if warns:
            lines.append("  경고:")
            for w in warns:
                lines.append(f"    ⚠️ {w}")
        return "\n".join(lines)
  • Function signature parameters define the input schema: skill_id (str, required), python_version (str, optional), os (str, optional), installed_packages (dict, optional), target_platform (str, optional). Docstring describes each parameter.
    def validate_compatibility(
        skill_id: str,
        python_version: str = "",
        os: str = "",
        installed_packages: dict = None,
        target_platform: str = "",
    ) -> str:
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It explains the check is based on python/packages and platform compatibility, and returns a summary with compatibility status, missing packages, and install recommendations. It implicitly indicates a read-only 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 concise, mixing English and Korean for clarity, and includes a structured Args section. A slight redundancy exists (e.g., 'before downloading' repeated) but overall efficient.

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, the description covers purpose, parameters, behavior, and return value (output schema exists, so return explanation is sufficient). It is complete for an agent to understand and invoke the tool.

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?

With 0% schema description coverage, the description fully compensates by listing each parameter with its purpose and format (e.g., python_version as '3.11.2', installed_packages as a dict). This adds meaning beyond the schema, enabling correct use.

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 platform before downloading. This distinguishes it from sibling tools like check_draft_status or download_skill, as it is a pre-download validation.

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 explicitly says 'before downloading' and includes Korean text reinforcing the timing. It does not state when not to use, but the context of siblings makes the usage clear and there are no direct alternatives.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/garasegae/aiskillstore'

If you have feedback or need assistance with the MCP directory API, please join our Discord server