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get_most_wanted

Identify skill gaps by retrieving zero-result search queries. Build these unbuilt skills to meet community demand.

Instructions

Get the list of most-wanted skills that haven't been built yet (Supply Loop). Agents can build these to fill community demand. / 미공급 수요 스킬 목록 (Most Wanted). 0건 검색 쿼리를 집계한 결과 — 여기 올라온 스킬을 만들어 업로드하면 즉시 다운로드 수요 있음.

Args: days: 최근 N일 (기본 30, 최대 365) limit: 최대 반환 개수 (기본 20, 최대 100) type: 'keyword' | 'capability' | 'all'

Returns: 수요 랭킹을 요약한 문자열. 각 항목: query, query_type, zero_result_count, last_seen.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
limitNo
typeNoall

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function for the 'get_most_wanted' MCP tool. Calls GET /v1/demand/most-wanted, formats results into a Korean-language summary string showing zero-result search demand queries ranked by frequency.
    def get_most_wanted(days: int = 30, limit: int = 20, type: str = "all") -> str:
        """
        Get the list of most-wanted skills that haven't been built yet (Supply Loop). Agents can build these to fill community demand. / 미공급 수요 스킬 목록 (Most Wanted).
        0건 검색 쿼리를 집계한 결과 — 여기 올라온 스킬을 만들어 업로드하면 즉시 다운로드 수요 있음.
    
        Args:
            days: 최근 N일 (기본 30, 최대 365)
            limit: 최대 반환 개수 (기본 20, 최대 100)
            type: 'keyword' | 'capability' | 'all'
    
        Returns:
            수요 랭킹을 요약한 문자열. 각 항목: query, query_type, zero_result_count, last_seen.
        """
        if type not in ("keyword", "capability", "all"):
            type = "all"
        result = _get("/v1/demand/most-wanted", params={"days": days, "limit": limit, "type": type})
        if result.get("status") == "error":
            return f"오류: {result.get('message')}"
        items = result.get("items", [])
        if not items:
            return "아직 누적된 0-건 검색 신호가 없습니다. 데이터 축적 중."
        lines = [f"Most Wanted — 최근 {days}일 누적 (type={type}, {len(items)}건):"]
        for i, it in enumerate(items, 1):
            lines.append(
                f"{i:>2}. [{it['query_type']}] \"{it['query']}\" × {it['zero_result_count']}"
                f" (last: {it.get('last_seen', '')[:10]})"
            )
        lines.append("")
        lines.append("만들어서 업로드 가이드: " + SKILL_STORE_URL + "/guide/usk")
        lines.append("업로드 시 X-Agent-Author 헤더로 attribution 기록 가능.")
        return "\n".join(lines)
  • Registration of the get_most_wanted function as an MCP tool via the @mcp.tool() decorator on a FastMCP instance.
    @mcp.tool()
    @_log_tool
  • Schema/input definition: parameters days (int, default 30), limit (int, default 20), type (str, 'keyword'|'capability'|'all'), returns a string.
    def get_most_wanted(days: int = 30, limit: int = 20, type: str = "all") -> str:
        """
        Get the list of most-wanted skills that haven't been built yet (Supply Loop). Agents can build these to fill community demand. / 미공급 수요 스킬 목록 (Most Wanted).
        0건 검색 쿼리를 집계한 결과 — 여기 올라온 스킬을 만들어 업로드하면 즉시 다운로드 수요 있음.
    
        Args:
            days: 최근 N일 (기본 30, 최대 365)
            limit: 최대 반환 개수 (기본 20, 최대 100)
            type: 'keyword' | 'capability' | 'all'
    
        Returns:
            수요 랭킹을 요약한 문자열. 각 항목: query, query_type, zero_result_count, last_seen.
        """
        if type not in ("keyword", "capability", "all"):
            type = "all"
        result = _get("/v1/demand/most-wanted", params={"days": days, "limit": limit, "type": type})
        if result.get("status") == "error":
            return f"오류: {result.get('message')}"
        items = result.get("items", [])
        if not items:
            return "아직 누적된 0-건 검색 신호가 없습니다. 데이터 축적 중."
        lines = [f"Most Wanted — 최근 {days}일 누적 (type={type}, {len(items)}건):"]
        for i, it in enumerate(items, 1):
            lines.append(
                f"{i:>2}. [{it['query_type']}] \"{it['query']}\" × {it['zero_result_count']}"
                f" (last: {it.get('last_seen', '')[:10]})"
            )
        lines.append("")
        lines.append("만들어서 업로드 가이드: " + SKILL_STORE_URL + "/guide/usk")
        lines.append("업로드 시 X-Agent-Author 헤더로 attribution 기록 가능.")
        return "\n".join(lines)
  • Helper function _get that makes the HTTP GET request to the Skill Store API. Used by get_most_wanted to call /v1/demand/most-wanted.
    def _get(path: str, params: dict = None) -> dict:
        url = SKILL_STORE_URL + path
        if params:
            url += "?" + urllib.parse.urlencode({k: v for k, v in params.items() if v is not None})
        try:
            with urllib.request.urlopen(url, timeout=10) as resp:
                return json.loads(resp.read().decode())
        except urllib.error.HTTPError as e:
            return {"status": "error", "message": f"HTTP {e.code}: {e.reason}"}
        except Exception as e:
            return {"status": "error", "message": str(e)}
  • Decorator helper that logs each MCP tool call to stdout with the tool name and argument keys.
    def _log_tool(fn):
        """각 MCP tool 호출을 stdout 에 한 줄 기록 — journalctl 에서 grep 가능.
        형식: TOOL_CALL tool=<name> kw=<arg_keys>  (PII 회피 위해 값은 로그 X)
        """
        @_functools_tool.wraps(fn)
        def _wrapper(*args, **kwargs):
            try:
                kw_keys = list(kwargs.keys())
                print(f"TOOL_CALL tool={fn.__name__} kw={kw_keys}", flush=True)
            except Exception:
                pass
            return fn(*args, **kwargs)
        return _wrapper
Behavior5/5

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

No annotations provided, but description fully covers what the tool does (returns a ranked summary of demanded skills), parameter constraints, and output format, with no contradictions.

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?

Extremely concise with clear language, Korean translation, and structured Args/Returns sections; every sentence adds value.

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?

Description adequately covers behavior and return format, but could mention that the output is a string summary (not structured data) given multiple siblings; still sufficient for a list retrieval.

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?

Description adds essential details to input schema parameters: min/max for days and limit, and valid enum values for type, compensating for 0% schema coverage.

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?

Description explicitly states it retrieves a list of most-wanted skills that haven't been built yet, clearly distinguishing it from sibling tools like search_skills or download_skill.

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?

Description indicates the tool is meant for agents to identify skills to build to meet community demand, but does not explicitly mention when not to use or compare with alternatives.

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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