Gracie Barra Jiu-Jitsu

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?

With no annotations provided, the description carries full burden and thoroughly discloses behavior: accent-insensitive and typo-tolerant matching, relevance ranking, server-side open_now computation, attribute matching (all must match), and detailed result structure including precomputed fields 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is fairly long but well-structured: purpose first, then matching behavior, filter composition, result format, and amenity slugs. Every sentence adds information, though minor trimming could improve conciseness.

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?

The tool has 6 parameters, no output schema, but the description thoroughly explains all parameters and the result format. It also covers edge cases like typo-tolerance and multi-condition filtering, making it complete for a search tool.

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?

Schema description coverage is 100% (baseline 3), but the description adds substantial value: explains q's typo-tolerance with examples, clarifies open_now filter behavior, details attribute matching logic and common slugs, and specifies substring matching for category and neighborhood. This goes well beyond the schema descriptions.

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 the tool searches for places (restaurants, cafes, shops) in a neighborhood, using a specific verb and resource. It distinguishes from siblings like search_dishes and get_place by focusing on general place search with multiple 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?

The description explains when to use this tool for multi-condition queries and lists filters, but does not explicitly exclude alternatives or provide when-not guidance. The context is clear, but no comparisons to similar search tools are given.

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 and action. Even similar operations like create_booking, request_reservation, reserve_event_tickets, and rsvp_event are clearly differentiated by their descriptions and the entities they handle (bookable services, restaurant tables, paid events, free events). No two tools appear to do the same thing.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with lowercase and underscores (e.g., create_booking, get_availability, list_events, search_places). This uniform convention makes the tool set predictable and easy for an agent to understand and select from.

Tool Count3/5

With 28 tools, the set is relatively large, exceeding the typical well-scoped range of 3-15. While each tool serves a specific purpose within the neighborhood directory domain, the count feels heavy and may overwhelm simple use cases. A reduction or grouping could improve coherence.

Completeness4/5

The tool set covers a broad range of user-facing operations for a neighborhood directory: searching places, browsing menus, booking services, reserving tables and event tickets, listing products and events, retrieving reviews and FAQs, and submitting forms. Minor gaps exist (e.g., no cancellation or create_review tool), but the core workflows are well-supported.

Resources

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