Tatuapé

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

A4.5/5.0

Behavior4/5

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

With no annotations, the description fully discloses key behaviors: typo-tolerance, precomputed open_now, delivery deep-links structure, hours format, and multi-condition filter semantics. It lacks pagination details but is otherwise transparent.

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 efficiently structured: purpose first, then matching behavior, filter usage, result fields, and amenity examples. Every sentence adds useful context with no redundancy.

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?

Despite no output schema, the description fully covers input parameters and output expectations (open_now boolean, attributes array, delivery links, rating, hours format). It is thorough for a search tool with 6 parameters.

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?

100% schema coverage provides parameter definitions, but the description adds significant value by explaining typo-tolerance behavior, providing filter composition examples, and listing common amenity slugs, exceeding basic 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 verb (search) and resource (places for restaurants, cafes, shops, etc.), and it distinguishes itself from sibling tools like search_dishes or get_place by being a general multi-filter 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?

Provides detailed usage instructions: accent-insensitive/typo-tolerant matching, filter composition with examples, and result field explanations. While it does not explicitly state when not to use it or list alternatives, the context and detail make the intended use 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

A4.1/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose, even among similar actions like booking, reservation, and ticket purchase—their descriptions clarify the domain and flow. No two tools could reasonably be confused.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., create_booking, get_availability, list_blog_posts). There are no deviations or mixed conventions.

Tool Count3/5

At 28 tools, the surface is larger than ideal for a single server, but each tool covers a distinct feature of the neighborhood directory (bookings, events, store, etc.). It borders on heavy but is still manageable for an agent.

Completeness5/5

The tool set covers the full lifecycle of browsing and acting on a neighborhood directory: search, details, reviews, menus, events, booking, reservations, store, loyalty, and more. No obvious gaps exist for the intended use case.

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