Pinheiros

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

A4.8/5.0

Behavior5/5

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

With no annotations, the description carries full burden. It discloses key behaviors: accent-insensitive and typo-tolerant matching, AND logic for attributes, precomputed open_now boolean, and result fields (delivery links, rating, hours). 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.

Conciseness5/5

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

The description is a single paragraph with no wasted words. Each sentence adds value: purpose, matching behavior, filter explanation, result structure, and common slugs. Information-dense yet easy to parse.

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 6 parameters and no output schema, the description covers all key aspects: matching algorithm, filter behavior, result fields (open_now, attributes, delivery, rating, hours), and amenity slugs. It provides sufficient context for an agent to invoke correctly.

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 coverage is 100%, and the description adds significant meaning: q matching details, open_now 'at this exact moment', attributes 'ALL must match', and result fields like delivery platform names and hours format. This greatly enriches the schema definitions.

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.)' with a specific verb and resource, and includes examples of place types. This distinguishes it from siblings like 'search_dishes' which focuses on dishes.

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 the tool's purpose and filter capabilities (category, neighborhood, open_now, attributes), implying when to use it for place searches. However, it does not explicitly state when not to use it or provide direct alternatives, though sibling tool names offer context.

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

A4.1/5.0

Disambiguation5/5

Each tool targets a distinct operation: bookings, restaurants, events, forms, etc. Even similar actions like create_booking and request_reservation are clearly differentiated by domain and description.

Naming Consistency5/5

Tools follow a consistent verb_noun pattern (e.g., create_booking, get_availability, list_blog_posts). Collection retrieval uses list_ while single item uses get_, which is a standard and clear convention.

Tool Count4/5

28 tools is on the upper end, but each serves a distinct function for a comprehensive neighborhood directory (places, events, booking, forms, etc.). No obvious bloat given the scope.

Completeness5/5

The tool surface covers all major user intents for a local directory: searching places, getting menus, booking services, viewing events, submitting forms, etc. No critical gaps are apparent.

Resources

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