Itaim Bibi

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

A4.4/5.0

Behavior4/5

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

With no annotations, the description discloses matching behavior (accent-insensitive, typo-tolerant), ranking by relevance, filter behavior (ALL must match for attributes), precomputed open_now, and output structure. It covers key behavioral traits.

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 concise and front-loaded with the purpose. Every sentence adds value, with no redundancy. Efficient use of space.

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?

Given 6 parameters, 0 required, and no output schema, the description explains all parameters and return data fields (open_now, attributes, delivery, hours, rating). Missing pagination details but still fairly complete.

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%, baseline 3. The description adds extra meaning: q is accent-insensitive and typo-tolerant, open_now computed at current time, attributes require ALL match, and gives common slugs. This adds value beyond the 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 clearly states the tool searches for places (restaurants, cafes, shops) in a neighborhood, with a specific verb and resource. It distinguishes itself from siblings like search_dishes and get_place by focusing on general place search.

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 the tool (for place search with filters) and provides context on matching behavior and filter composition. It does not explicitly state when not to use it or mention alternatives, but the context is clear.

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: appointments, products, events, places, menus, reviews, forms, etc. No two tools have overlapping purposes; even similar booking tools (create_booking, request_reservation, reserve_event_tickets, rsvp_event) are clearly differentiated by description and use case.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using lowercase with underscores (e.g., create_booking, list_events, search_places). Verbs are appropriate (create, get, list, search, submit, request, reserve, rsvp) and nouns clearly indicate the resource.

Tool Count4/5

28 tools cover a broad domain (neighborhood directory with Wix integrations). While each tool has a clear purpose, the count exceeds the typical 3-15 range slightly, making the surface feel somewhat heavy but still manageable.

Completeness5/5

The tool surface provides comprehensive coverage for the domain: discovery (search places, list events, list guides), details (get place, get reviews), booking (appointments, restaurant, event tickets, RSVP), checkout, forms, FAQs, loyalty, pricing plans, and more. No obvious dead ends for common user journeys.

Resources

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