Bom Retiro

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

A4.6/5.0

Behavior5/5

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

With no annotations, the description carries full burden and excels: it discloses accent-insensitive and typo-tolerant matching, relevance ranking, precomputed open_now boolean, attributes array, delivery deep-links (with platforms), rating, and structured hours. It also lists common amenity slugs. This provides comprehensive behavioral insight for an AI agent.

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 three sentences: first states purpose, second details search behavior, third explains filters and result fields. It is well-structured and front-loaded with the most important information. It could be slightly more concise by combining some details, but overall it is efficient and clear.

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 the tool has 6 parameters (none required), high schema coverage, and no output schema, the description compensates fully by detailing the output structure (open_now, attributes, delivery, rating, hours). It covers all major aspects: filters, search behavior, and result fields. The agent has enough information to use the tool correctly without needing additional documentation.

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?

Schema coverage is 100% with each parameter described. The description adds significant value beyond the schema: it explains that 'q' is accent-insensitive and typo-tolerant, that 'open_now' computes current local time server-side, that 'attributes' requires ALL slugs to match, and it gives examples of common amenity slugs. This enriches the semantic understanding for the agent.

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 'Search for places (restaurants, cafes, shops, etc.) in this neighborhood.' It uses a specific verb ('search') and resource ('places'), and distinguishes itself from sibling tools like search_dishes or list_categories by focusing on a general place search with neighborhood context.

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 explains when to use this tool (to search for places with various filters) and provides details on composing filters. It does not explicitly mention when not to use or contrast with alternatives, but the context from sibling tools makes it clear that this is for broad place search while others handle specific operations like bookings or dish search. Slight room for improvement in explicit 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

A4/5.0

Disambiguation5/5

Each tool targets a distinct domain (bookings, store, events, restaurants, movies, etc.) with detailed descriptions that clearly differentiate their purposes. Overlap is minimal and handled by context, such as create_booking vs create_checkout vs request_reservation.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case, using prefixes like get_, list_, create_, search_, etc. The naming is predictable and makes it easy to infer the action and resource.

Tool Count4/5

28 tools is above the typical well-scoped range, but each tool covers a distinct feature of the neighborhood directory (places, events, movies, blog, forms, etc.). The count reflects the integration of multiple Wix apps, which is appropriate for a comprehensive directory.

Completeness4/5

The tool set covers most user-facing operations for a directory: search places, get details, book services, events, restaurant menus, etc. Minor gaps exist (e.g., no cancellation for bookings, no update operations), but these are likely out of scope for a public-facing server.

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