Olé Futebol

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

A4.7/5.0

Behavior5/5

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

No annotations provided, so the description fully covers behavioral traits: typo-tolerance, accent-insensitivity, relevance ranking, precomputed open_now, delivery deep-link structure, and hour format. 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?

The description is detailed but efficient, front-loading the core purpose and then detailing filters and result structure. Every sentence contributes useful information, though it could be slightly tighter.

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?

Without an output schema, the description fully explains all return fields: open_now boolean, attributes array, delivery array (with platform and url), rating, and structured hours (day 0-6, time HHMM). Parameter usage is clear with 6 optional parameters well documented.

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?

Despite 100% schema coverage, the description adds significant value beyond the schema: explains typo-tolerance for q, 'ALL must match' for attributes, the current-moment meaning of open_now, delivery object structure ({platform, url}), and common amenity slugs. Each parameter is enriched.

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 places (restaurants, cafes, shops, etc.)' with a specific verb and resource. The description distinguishes this from sibling tools like 'get_place' (specific place) or 'list_categories' (categories only) by emphasizing free-text search with filters.

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?

Provides detailed guidance on how to use filters (category, neighborhood, open_now, attributes) and the capabilities of the q parameter (accent-insensitive, typo-tolerant). However, it does not explicitly state when to prefer this tool over alternatives like get_place or list_categories.

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 resource or action (booking, checkout, availability, events, reviews, etc.). Even similar-looking tools like create_booking, request_reservation, and rsvp_event are clearly differentiated by their descriptions and target systems.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., create_booking, get_availability, list_booking_services). No mixing of conventions or unclear verbs.

Tool Count4/5

28 tools is on the higher end, but the server covers a wide domain (places, events, movies, booking, forms, loyalty, etc.). Each tool serves a clear purpose, so the count is justifiable, though minor trimming could be considered.

Completeness4/5

The tool set covers most workflows for a neighborhood directory: search, details, booking, events, reviews, etc. Some CRUD operations are missing (no update/delete for bookings), but these are consumer-facing actions where that is acceptable. The domain is well-covered.

Resources

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