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get_event_info

Retrieves details of a specific event by its ID, with optional custom field selection.

Instructions

Event Info

Retorna informações de um evento.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
event_idYesID do evento
selectNoSeleção de campos customizados na resposta

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the get_event_info tool. It makes an HTTP GET request to /events/api/v1/{event_id}/info and returns the JSON response.
    async def get_event_info(
        event_id: int,
        select: Optional[str] = None,
    ) -> str:
        """Event Info
        
        Retorna informações de um evento.
        
        Args:
            event_id: ID do evento
            select: Seleção de campos customizados na resposta"""
        endpoint = f"/events/api/v1/{event_id}/info"
        params = {}
        if select is not None:
            params["select"] = select
        result = await get_client().get(endpoint, params=params)
        return json.dumps(result, indent=2)
  • Exports get_event_info in the __all__ list, making it discoverable by the auto-registration mechanism in server.py.
    __all__ = ["get_event_info", "get_event_participants"]
  • Generic tool discovery and registration: iterates over all tool modules (including tickets.py) and registers every public async function as an MCP tool via mcp.tool()(obj).
    def _discover_and_register_tools() -> int:
        """Import all modules under hotmart_mcp.tools and register async functions."""
        registered = 0
    
        for module_info in pkgutil.iter_modules(tools_pkg.__path__, prefix=f"{tools_pkg.__name__}."):
            if module_info.name.endswith("__init__"):
                continue
    
            module = importlib.import_module(module_info.name)
    
            for name, obj in inspect.getmembers(module, iscoroutinefunction):
                if name.startswith("_"):
                    continue
                mcp.tool()(obj)
                registered += 1
    
        return registered
  • Shared helper providing get_client() lazy singleton used by get_event_info to make HTTP requests.
    """Shared lazy singleton for the Hotmart API client."""
    
    from __future__ import annotations
    
    from hotmart_mcp.client import HotmartClient
    
    _client: HotmartClient | None = None
    
    
    def get_client() -> HotmartClient:
        global _client
        if _client is None:
            _client = HotmartClient()
        return _client
  • Re-exports all tool functions from tickets.py (including get_event_info) via wildcard import, enabling discovery by server.py.
    """Auto-generated: re-exports all tool functions."""
    
    from .club import *  # noqa: F401,F403
    from .coupons import *  # noqa: F401,F403
    from .negotiation import *  # noqa: F401,F403
    from .products import *  # noqa: F401,F403
    from .sales import *  # noqa: F401,F403
    from .subscriptions import *  # noqa: F401,F403
    from .tickets import *  # noqa: F401,F403
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 only says 'returns information,' which implies a read operation but discloses no error handling, performance traits, or side effects. Minimal transparency for a simple getter.

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 very concise, only two lines. It is front-loaded with the title 'Event Info.' Although brief, it wastes no words and is appropriately sized for a simple tool, though it could include slight additions.

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?

Given the output schema exists, the description need not explain return values. However, it lacks context about the event domain and fails to distinguish from sibling tools. Adequate for a trivial operation but not comprehensive.

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

Parameters3/5

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

Schema coverage is 100%, with both event_id and select having descriptions in the schema. The tool description adds no further meaning or context for the parameters, so it meets the baseline but does not enhance understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns event information, matching the tool's name and purpose. However, it does not differentiate from sibling tools like get_event_participants, missing an opportunity to clarify scope.

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?

No guidance on when to use this tool versus alternatives. The description lacks context about selecting this over get_event_participants or other event-related tools, leaving the agent without decision clues.

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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