Liberdade

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

Name	Required	Description
`q`	No	Free-text term matched against place name and address — accent-insensitive and typo-tolerant.
`limit`	No	Max results to return (default 20, max 100).
`category`	No	Filter by category label (e.g. "Café"). Case-insensitive substring match.
`open_now`	No	When true, return only places open at the current local time (computed server-side from their opening hours).
`attributes`	No	Amenity 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.
`neighborhood`	No	Filter by neighborhood name. Case-insensitive substring match.

TDQS

A4.1/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. Discloses accent-insensitive and typo-tolerant matching, filter composition details, and result structure. Could mention idempotency but overall adequate.

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?

Description is moderately long but each sentence provides specific detail (matching behavior, filter composition, result fields). No fluff; could tighten slightly but well-structured.

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?

No output schema, but description explains return fields (open_now boolean, attributes array, delivery array, rating, structured hours) adequately for a search tool. Covers essential behavioral context.

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?

Input schema covers 100% of parameters, description adds meaning beyond schema: accent-insensitive for q, composition for filters, and common amenity slugs list. Adds value without redundancy.

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?

Description clearly states the tool searches for places (restaurants, cafes, shops) in a neighborhood, distinguishing it from sibling tools like get_place (single place) and search_dishes (food). Uses specific verb 'search' and resource 'places'.

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?

Implied usage context (neighborhood search) but no explicit when-to-use or when-not-to-use compared to alternatives like get_place or list_categories. Missing exclusions.

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

A4/5.0

Disambiguation5/5

Each tool targets a distinct domain (e.g., bookings, events, places, restaurants, movies, forms) with clear boundaries. Even similar actions like create_booking and request_reservation are differentiated by service type (appointment vs restaurant table) and payment flow.

Naming Consistency5/5

All 28 tools follow the consistent verb_noun snake_case pattern (e.g., create_booking, list_events, search_places). Verbs are appropriate and predictable, with no mixing of conventions.

Tool Count4/5

28 tools is slightly above the typical range, but each tool serves a distinct purpose within the comprehensive neighborhood directory domain. The count is justified by the breadth of features (booking, events, restaurants, movies, etc.).

Completeness4/5

The tool surface covers most user needs: searching, viewing details, booking, reservations, events, forms, and more. Minor gaps exist, such as no cancellation tools for bookings/reservations, but the core workflows are well-supported.

Resources

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