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What's Up In: local events

by ai.whatsupin
OverviewDiscussionsAdmin

List upcoming events in one city

list_upcoming
Read-onlyIdempotent

The next N days of events in one What's Up In city, optionally one category, topic or place. by_day counts every match per date; over limit, the rows shown are spread across the days.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYesWhich city (name or slug): Boston, Chicago, Los Angeles, Orange County, Sacramento, San Diego, San Francisco, Seattle, Atlanta, Cancún, Cleveland, Dallas-Fort Worth, DC, Denver, Detroit, Honolulu, Las Vegas, London, Mexico City, Miami, Nashville, New York, Philadelphia, Phoenix, Pittsburgh, Portland, Rome, Tokyo. list_cities has the full list.
daysNoWindow in days (default 7)
nearNoEvents within radius_km of a place in the city: a neighborhood, a town, a venue name, or "lat,lon". Results get distance_km, nearest first within each day.
limitNoMax rows (default 50)
topicNo
categoryNo
radius_kmNoDistance for near (default 1.5, max 50), or how far neighborhood reaches past its boundary (default 0)
neighborhoodNoOnly events in this neighborhood or town of the city, e.g. 'Capitol Hill', 'Silver Lake', 'Cambridge'. Events whose venue isn't mapped can't be placed and are left out (place.unplaced_events counts them).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / city / description
      Previous value: -"Which city (name or slug): Boston, Chicago, Los Angeles, Orange County, Sacramento, San Diego, San Francisco, Seattle, Cancún, Cleveland, Dallas-Fort Worth, DC, Denver, Detroit, Honolulu, Mexico City, Miami, Nashville, New York, Philadelphia, Phoenix, Pittsburgh, Portland, Tokyo. list_cities has the full list."New value: +"Which city (name or slug): Boston, Chicago, Los Angeles, Orange County, Sacramento, San Diego, San Francisco, Seattle, Atlanta, Cancún, Cleveland, Dallas-Fort Worth, DC, Denver, Detroit, Honolulu, Las Vegas, London, Mexico City, Miami, Nashville, New York, Philadelphia, Phoenix, Pittsburgh, Portland, Rome, Tokyo. list_cities has the full list."
  2. Changed1 schema field changed
    • changedInput schema / properties / city / description
      Previous value: -"Which city (name or slug): Boston, Chicago, Los Angeles, Orange County, Sacramento, San Diego, San Francisco, Seattle, Cancún, Cleveland, Dallas-Fort Worth, DC, Denver, Detroit, Honolulu, Mexico City, Miami, Nashville, New York, Philadelphia, Pittsburgh, Portland, Tokyo. list_cities has the full list."New value: +"Which city (name or slug): Boston, Chicago, Los Angeles, Orange County, Sacramento, San Diego, San Francisco, Seattle, Cancún, Cleveland, Dallas-Fort Worth, DC, Denver, Detroit, Honolulu, Mexico City, Miami, Nashville, New York, Philadelphia, Phoenix, Pittsburgh, Portland, Tokyo. list_cities has the full list."
  3. Changed1 schema field changed
    • changedInput schema / properties / city / description
      Previous value: -"Which city (name or slug): Boston, Chicago, Los Angeles, Orange County, Sacramento, San Diego, San Francisco, Seattle, Cancún, Cleveland, Dallas-Fort Worth, DC, Denver, Detroit, Honolulu, Mexico City, Nashville, New York, Philadelphia, Pittsburgh, Portland, Tokyo. list_cities has the full list."New value: +"Which city (name or slug): Boston, Chicago, Los Angeles, Orange County, Sacramento, San Diego, San Francisco, Seattle, Cancún, Cleveland, Dallas-Fort Worth, DC, Denver, Detroit, Honolulu, Mexico City, Miami, Nashville, New York, Philadelphia, Pittsburgh, Portland, Tokyo. list_cities has the full list."
  4. Changed1 schema field changed
    • changedInput schema / properties / city / description
      Previous value: -"Which city (name or slug): Boston, Chicago, Los Angeles, Orange County, Sacramento, San Diego, San Francisco, Seattle, Cancún, Dallas-Fort Worth, DC, Denver, Detroit, Honolulu, Mexico City, Nashville, New York, Philadelphia, Phoenix, Portland, Tokyo. list_cities has the full list."New value: +"Which city (name or slug): Boston, Chicago, Los Angeles, Orange County, Sacramento, San Diego, San Francisco, Seattle, Cancún, Cleveland, Dallas-Fort Worth, DC, Denver, Detroit, Honolulu, Mexico City, Nashville, New York, Philadelphia, Pittsburgh, Portland, Tokyo. list_cities has the full list."
  5. Changed1 schema field changed
    • changedInput schema / properties / city / description
      Previous value: -"Which city (name or slug): Boston, Chicago, Los Angeles, Orange County, Sacramento, San Diego, San Francisco, Seattle, Dallas-Fort Worth, DC, Honolulu, New York, Philadelphia, Portland. list_cities has the full list."New value: +"Which city (name or slug): Boston, Chicago, Los Angeles, Orange County, Sacramento, San Diego, San Francisco, Seattle, Cancún, Dallas-Fort Worth, DC, Denver, Detroit, Honolulu, Mexico City, Nashville, New York, Philadelphia, Phoenix, Portland, Tokyo. list_cities has the full list."
  6. Changed1 schema field changed
    • addedInput schema / properties / limit
      Added value: +{
+  "description": "Max rows (default 50)",
+  "maximum": 100,
+  "minimum": 1,
+  "type": "integer"
+}
  7. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive behavior, so safety is covered. The description adds genuine behavioral context beyond that: 'by_day' counts every match per date, and when over the limit, rows are spread across days rather than truncated arbitrarily. That is real disclosure the annotations do not provide.

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?

Two tight sentences with the window/scope constraint front-loaded and the result-shaping caveat second. No filler, though the 'by_day' term is introduced without definition.

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?

With no output schema, the description carries some burden for explaining the result shape, and it partially does via by_day and the limit-spreading note. However, an 8-parameter tool with no return documentation leaves gaps about what a row actually contains.

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 description coverage is 75%, so most parameters are documented structurally. The description only adds that category, topic, and place filters are mutually 'optionally one', which is marginal beyond the schema. It does not explain the referenced 'by_day' grouping conceptually.

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?

States a specific verb and resource with scope: 'next N days of events in one What's Up In city'. The 'one city' phrasing implicitly separates it from search_across_cities, though the sibling is never named. Clear enough for an agent to identify the operation.

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 when to use it (single-city browsing) but never states alternatives or exclusions versus search_events, get_event, or search_across_cities. Usage must be inferred from the scope wording.

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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