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njoerd114

kubecon-eu-mcp

by njoerd114

find_parties

Discover conference parties, happy hours, and social events at KubeCon Europe. Filter by day or time to find networking opportunities with sponsor details, locations, and RSVP links.

Instructions

Find conference parties, happy hours, and social events.

Args: day: Optional day filter: "monday", "tuesday", "wednesday", "thursday". after: Optional time filter — only show events starting after this time (e.g., "6PM"). before: Optional time filter — only show events starting before this time (e.g., "10PM").

Returns: JSON array of parties with name, time, sponsor, location, and RSVP link.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dayNo
afterNo
beforeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The find_parties tool implementation which fetches parties from a data service and formats them as a JSON string.
    @mcp.tool()
    async def find_parties(day: str = "", after: str = "", before: str = "") -> str:
        """Find conference parties, happy hours, and social events.
    
        Args:
            day: Optional day filter: "monday", "tuesday", "wednesday", "thursday".
            after: Optional time filter — only show events starting after this time (e.g., "6PM").
            before: Optional time filter — only show events starting before this time (e.g., "10PM").
    
        Returns:
            JSON array of parties with name, time, sponsor, location, and RSVP link.
        """
        if day:
            parties = await data_service.get_parties_for_day(day)
        else:
            parties = await data_service.get_parties()
    
        if not parties:
            return json.dumps(
                {
                    "message": "No party data available. Try again later or check https://conferenceparties.com/kubeconeu26/"
                }
            )
    
        return json.dumps([p.to_dict() for p in parties], indent=2)
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 describes the tool as a find/query operation (implied read-only) and specifies the return format, which is helpful. However, it lacks details on permissions, rate limits, error handling, or whether results are paginated—important behavioral traits for an agent to use it effectively.

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?

The description is efficiently structured: a clear purpose statement followed by bullet-point-like sections for Args and Returns. Every sentence adds value—no fluff or repetition. It's front-loaded with the core functionality and uses formatting to enhance readability.

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?

Given the tool's moderate complexity (3 optional parameters) and the presence of an output schema (implied by 'Returns' details), the description is reasonably complete. It covers purpose, parameters, and return format. However, without annotations, it could benefit from more behavioral context (e.g., data freshness, limitations) to be fully comprehensive.

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?

Schema description coverage is 0%, so the description must compensate. It does this well by explaining all three parameters: 'day' with allowed values, 'after' and 'before' with examples and purpose. This adds significant meaning beyond the bare schema, though it could specify time format constraints more precisely (e.g., 24-hour vs. 12-hour).

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 the tool's purpose: 'Find conference parties, happy hours, and social events.' It uses a specific verb ('Find') and identifies the resource type (social events at a conference). However, it doesn't explicitly distinguish this from sibling tools like 'get_colocated_events' or 'plan_party_route', which might have overlapping functionality.

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. With siblings like 'get_colocated_events' and 'plan_party_route' that might relate to events or parties, there's no indication of how this tool differs or when it's preferred. The usage context is implied by the tool name but not explicitly stated.

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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