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njoerd114

kubecon-eu-mcp

by njoerd114

find_speaker

Find sessions featuring a specific speaker at KubeCon Europe 2026. Provide a speaker name or partial name to retrieve their sessions.

Instructions

Find sessions by a specific speaker.

Args: name: Speaker name or partial name (e.g., "Lin Sun", "Bryce").

Returns: JSON array of sessions featuring the speaker.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool handler for 'find_speaker'. Decorated with @mcp.tool(), takes a speaker name, calls data_service.find_speakers(), and returns JSON results.
    @mcp.tool()
    async def find_speaker(name: str) -> str:
        """Find sessions by a specific speaker.
    
        Args:
            name: Speaker name or partial name (e.g., "Lin Sun", "Bryce").
    
        Returns:
            JSON array of sessions featuring the speaker.
        """
        results = await data_service.find_speakers(name)
        if not results:
            return json.dumps(
                {
                    "message": f"No sessions found for speaker '{name}'.",
                    "suggestion": "Try a partial name or check spelling.",
                }
            )
        return json.dumps([s.to_dict() for s in results], indent=2)
  • The data service method find_speakers() that searches sessions by speaker name (partial match) and also checks session titles.
    async def find_speakers(self, name: str, limit: int = 10) -> list[Session]:
        """Find sessions by speaker name."""
        all_sessions = await self.get_sessions()
        name_lower = name.lower()
        results = []
    
        for s in all_sessions:
            for speaker in s.speakers:
                if name_lower in speaker.lower():
                    results.append(s)
                    break
            else:
                # Also check the title (speaker names often in title)
                if name_lower in s.title.lower():
                    results.append(s)
    
        return results[:limit]
  • The FastMCP server instance (mcp) is created with tool instructions listing 'find_speaker' as one of the available tools. The @mcp.tool() decorator on line 112 registers it.
    mcp = FastMCP(
        "KubeCon EU 2026 Guide",
        stateless_http=True,
        json_response=True,
        instructions="""
    # KubeCon + CloudNativeCon Europe 2026 — Conference Guide
    
    You are a helpful conference assistant for KubeCon EU 2026, held March 23-26
    at RAI Amsterdam, Netherlands.
    
    ## Tool Selection Guide
    
    - Use `search_sessions` to find talks by topic, speaker, or keyword.
    - Use `get_schedule` to see the full schedule for a specific day.
    - Use `find_parties` to discover social events and parties.
    - Use `plan_party_route` to optimize an evening of party-hopping.
    - Use `get_venue_info` to answer questions about the venue, rooms, or maps.
    - Use `get_hotel_info` for hotel details and distances to venue.
    - Use `get_travel_info` for transit, airport, and airline discount info.
    - Use `get_colocated_events` for Monday's co-located events.
    - Use `find_speaker` to look up what a specific person is presenting.
    - Use `get_conference_overview` for a high-level event summary.
    - Use `score_sessions` to get sessions with a scoring rubric for personalized ranking.
    - Use `detect_conflicts` to check if selected sessions overlap in time.
    
    ## Important Context
    
    - All times are in CET (Central European Time, UTC+1).
    - Monday (March 23) = Co-located Events day (requires All Access Pass).
    - Tuesday-Thursday (March 24-26) = Main conference (keynotes + breakouts).
    - KubeCrawl + CloudNativeFest is Tuesday 5:30-7PM in the expo halls.
    - Session seating is first-come, first-served.
    - Recordings are posted on the CNCF YouTube channel within 2 weeks.
    """,
    )
Behavior3/5

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

No annotations are provided, and the description does not explicitly state side effects or safety, but the function is clearly a read-only search.

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 with three sentences, though it could be slightly more compact.

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?

With an output schema present and a simple tool, the description sufficiently explains the return as 'JSON array of sessions' and covers the parameter.

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?

The schema has 0% coverage, but the description adds that 'name' is a 'Speaker name or partial name' with examples, adding meaningful context.

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 'Find sessions by a specific speaker' and provides examples, distinguishing it from sibling tools like find_parties or search_sessions.

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

Usage Guidelines3/5

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

The description implies usage when searching by speaker name but lacks explicit guidance on when not to use or 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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