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search_places

Search for places (restaurants, cafes, shops, etc.) in this neighborhood. The q match is accent-insensitive and typo-tolerant ("acai" finds "Açaí", "restaurnt" finds "Restaurante"), ranked by relevance. Compose filters for multi-condition queries: category, neighborhood, open_now (true = open at this exact moment), and attributes (amenity slugs, ALL must match). Each result includes a precomputed open_now boolean, an attributes array (amenity slugs), a delivery array of curated delivery-app deep-links (ifood, rappi, 99food, uber-eats — each { platform, url }), rating, and structured hours (Google Maps periods: day 0=Sun–6=Sat, time "HHMM"). Common amenity slugs: wifi, outdoor-seating, wheelchair, dog-friendly, delivery, takeaway, reservations, live-music, vegan, gluten-free (availability varies per place).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFree-text term matched against place name and address — accent-insensitive and typo-tolerant.
limitNoMax results to return (default 20, max 100).
categoryNoFilter by category label (e.g. "Café"). Case-insensitive substring match.
open_nowNoWhen true, return only places open at the current local time (computed server-side from their opening hours).
attributesNoAmenity slugs a place must ALL have, e.g. ["wifi","outdoor-seating"]. Common slugs: wifi, outdoor-seating, wheelchair, dog-friendly, delivery, takeaway, reservations, live-music, vegan, gluten-free.
neighborhoodNoFilter by neighborhood name. Case-insensitive substring match.

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations provided, the description fully carries the transparency burden. It discloses accent-insensitive and typo-tolerant matching, relevance ranking, the exact semantics of 'open_now', the ALL-must-match behavior of attributes, and the detailed result shape including hours format and delivery links.

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 dense but every sentence earns its place. It front-loads the core purpose and then efficiently covers query behavior, filters, result fields, and common slugs without unnecessary fluff. The length is appropriate given the tool's complexity and the lack of an output schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given six parameters, no output schema, and no annotations, the description is remarkably complete. It explains not only how to invoke the tool but also what the response contains, including nested structures like the delivery array and hours periods, so an agent can use the results correctly.

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?

Although the schema already describes every parameter, the description adds substantial meaning: examples of typo tolerance, the definition of 'open_now' as 'open at this exact moment', the requirement that attributes ALL match, and a list of common amenity slugs. This goes well beyond the structured 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?

The description opens with a specific verb and resource: 'Search for places (restaurants, cafes, shops, etc.)'. It distinguishes itself from siblings like search_dishes by targeting places generally and by describing the available filters, making the tool's purpose unambiguous.

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

Usage Guidelines4/5

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

The description provides clear context: it is for searching places in a neighborhood and supports multi-condition filters. However, it does not explicitly state when to use this tool over siblings such as search_dishes or list_categories, so it stops short of full alternative-based guidance.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct resource or action (e.g., create_booking vs create_checkout, reserve_event_tickets vs rsvp_event), and descriptions provide clear boundaries. Even closely related tools like list_booking_services and list_pricing_plans are differentiated by context.

Naming Consistency5/5

All 26 tools follow a consistent snake_case verb_noun pattern (get_, list_, create_, search_, etc.), with no mixed conventions or vague verbs. This makes the tool set predictable and easy to navigate.

Tool Count3/5

At 26 tools, the set is on the heavy side and exceeds the typical well-scoped range, but the diversity reflects a multi-featured directory covering bookings, events, store, reviews, forms, and more. Each tool appears to serve a distinct purpose, so the count is borderline rather than excessive.

Completeness4/5

The surface covers the core user workflows: search, view details, book services, reserve/RSVP events, request reservations, submit forms, and browse content. Minor gaps exist (e.g., no cancellation/update for bookings, no full blog post body), but these are workarounds and not critical for a consumer-facing directory.

Resources