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get_agent_author_stats

Get contribution stats for an agent author: uploads, claims, downloads, average rating, and top categories.

Instructions

Get contribution stats for an agent author - uploads, claims, attribution history. / 에이전트 빌더 기여 통계.

Args: agent_name: 에이전트 이름 (예: "claude-sonnet-4-6")

Returns: skills_count, total_downloads, downloads_7d, avg_rating, top_categories 요약.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function that executes the get_agent_author_stats tool logic. It calls the API endpoint /v1/agent-authors/{agent_name}/stats and formats the response into a human-readable summary.
    def get_agent_author_stats(agent_name: str) -> str:
        """
        Get contribution stats for an agent author - uploads, claims, attribution history. / 에이전트 빌더 기여 통계.
    
        Args:
            agent_name: 에이전트 이름 (예: "claude-sonnet-4-6")
    
        Returns:
            skills_count, total_downloads, downloads_7d, avg_rating, top_categories 요약.
        """
        result = _get(f"/v1/agent-authors/{agent_name}/stats")
        if result.get("status") == "error":
            return f"오류: {result.get('message')}"
        s = result.get("stats", {})
        if s.get("skills_count", 0) == 0:
            return f"에이전트 '{agent_name}' 은(는) 아직 업로드한 스킬이 없습니다."
        return (
            f"Agent: {s['name']}\n"
            f"  Skills published: {s['skills_count']}\n"
            f"  Total downloads:  {s['total_downloads']}\n"
            f"  Downloads (7d):   {s['downloads_7d']}\n"
            f"  Avg rating:       {s['avg_rating']}\n"
            f"  Top categories:   {', '.join(s['top_categories']) if s['top_categories'] else '—'}\n"
            f"  Latest skill at:  {s.get('latest_skill_packaged_at', '—')}"
        )
  • The @mcp.tool() decorator registers this function as an MCP tool with the FastMCP server instance.
    @mcp.tool()
  • The _get helper function is used by the handler to make GET requests to the SKILL_STORE_URL API endpoint.
    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)}
Behavior2/5

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 does not disclose whether the tool is read-only, requires authentication, or has side effects. Being a 'get' tool implies read-only but is not explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively short and includes both English and Korean, but the Korean parts are somewhat redundant. The Args/Returns structure is clear.

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

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and an existing output schema mentioned, the description covers purpose, parameter, and returns. However, it lacks usage guidelines and behavioral transparency, limiting completeness.

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?

With 0% schema description coverage, the description adds valuable context for the single parameter 'agent_name' by providing an example and Korean explanation, going beyond the schema's title.

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's function: retrieving contribution stats for an agent author, specifying items like uploads, claims, and attribution history. It distinguishes from sibling tools like get_agent_identity_stats.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, nor does it mention any prereq conditions or exclusions. It simply describes what it does.

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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