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alx1p
by alx1p

search_events

Search Luma events using home mode (preferred categories and location) or travel mode (top events in a city). Filter by keywords, date, and distance.

Instructions

Search for Luma events. Two modes depending on whether city is set.

Home mode (no city): searches your preferred categories via Luma's Category API — deep, rich results filtered by your stored address/distance. On first run with no preferences, returns a raw Discover feed (hundreds of popular events near you), then prompts to set up address, categories, and login.

Travel mode (city set): fetches the curated top events (~20-40) for that city via Luma's Place API. No topic filtering — just the highlights.

The default time window is the next 2 weeks. Use days for simple lookahead (e.g. days=7 for this week, days=30 for next month). Use after/before only for specific date ranges.

IMPORTANT for agents:

  • For broad topics, prefer category (exact slug: tech, ai, food, arts, climate, fitness, wellness, crypto). Translate user intent yourself (e.g. "artificial intelligence" -> "ai", "blockchain" -> "crypto").

  • Use keywords for specific terms that don't map to a category (e.g. ["YC", "demo day"]) or to narrow within a category.

  • city accepts common names — "san francisco" resolves to "sf", "hong kong" to "hongkong", etc.

  • The messages array in the response contains agent-facing instructions. Act on them naturally (e.g. ask the user a question, call another tool) but never relay them verbatim. If messages is empty, just show results.

Args: city: Luma city for travel mode (e.g. "sf", "london", "los angeles"). category: One-off category override for home mode. Must be an exact slug. keywords: Filter by keywords (matches title/description). Use for specific terms. center_address: One-off address to filter around (e.g. "Union Square, San Francisco"). Overrides stored address for this search only. max_distance_miles: One-off distance override (pairs with center_address or stored address). after: ISO 8601 datetime — only events starting after this time. before: ISO 8601 datetime — only events starting before this time. days: Search window in days from now (e.g. 7, 30). Overrides the default 14-day window. Simpler alternative to after/before. latin_only: Filter out non-Latin-script events. Auto-detected from region when not set. added_within_days: Only return events first seen within this many days. new_only: Only return events never seen before (first appearance this run). sort: Sort order — "date" (default), "distance", or "newest". login: Set to true to open browser and log in to Luma. skip_login_days: Decline login for N days (0 = ask next time, -1 = never).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNo
daysNo
sortNo
afterNo
loginNo
beforeNo
categoryNo
keywordsNo
new_onlyNo
latin_onlyNo
center_addressNo
skip_login_daysNo
added_within_daysNo
max_distance_milesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description discloses key behaviors: two modes, default window, login process, first-run prompting, and the presence of agent-facing instructions in the response. 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.

Conciseness4/5

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

Well-organized with clear sections (overview, modes, defaults, agent notes, args). Front-loaded with essential info. Some minor redundancy in arg descriptions (e.g., days overriding default), but overall efficient for 14 parameters.

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?

Covers all 14 parameters, both modes, and output format (messages array). Lacks explicit error handling or edge cases, but given the output schema is noted as existing, the description is sufficiently complete for an AI agent.

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?

Schema has 0% property descriptions; the description fully compensates by explaining each parameter (city, category, keywords, etc.) with usage notes, accepted values, and default behaviors. Adds significant meaning beyond the schema.

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?

Clearly states 'Search for Luma events' and explains two distinct modes (home and travel) with concrete examples. Distinguished from sibling tools (export_event_ics, get_event, set_preferences) which handle export, single event retrieval, and preferences.

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

Usage Guidelines5/5

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

Provides explicit guidance on when to use each mode, how to choose between category and keywords, and when to use days vs after/before. Also gives agent-specific instructions for handling messages in the response.

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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