Paraíso

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

A4.3/5.0

Behavior4/5

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

The description discloses accent-insensitive and typo-tolerant matching, filter composition (ALL must match), server-side open_now computation, and return fields (open_now, attributes, delivery links, hours). No annotation contradictions. However, lacks rate limits, pagination, or auth requirements.

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 front-loaded with purpose, then efficient details. Every sentence adds value: matching behavior, filter usage, output format, amenity examples. No redundant or extraneous text.

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 optional parameters and no output schema, the description explains return fields (open_now, attributes, delivery, rating, hours) and their structure (e.g., 'hours: Google Maps periods: day 0=Sun–6=Sat, time HHMM'). Covers common amenity slugs and platform details. Very thorough.

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?

With 100% schema coverage, baseline is 3. The description adds nuance: typo-tolerant examples for 'q', explains filter composition, specifies 'open at this exact moment' for open_now, and lists common amenity slugs. Adds value beyond the schema's property 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 starts with a clear action verb ('Search for places') and specifies the resource ('places in this neighborhood'), distinguishing it from sibling tools like 'get_place' (single place) and 'search_dishes' (specific to dishes).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context on when to use (text search with filters) but does not explicitly exclude other tools or give alternative usage guidance. The 'in this neighborhood' phrase implies location context but no direct comparison to siblings.

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 domain (booking, store checkout, events, restaurant, etc.) with clear descriptions. No two tools have overlapping purposes; even similar actions like create_booking, create_checkout, and reserve_event_tickets are differentiated by their specific contexts.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with underscores (e.g., get_availability, list_blog_posts, reserve_event_tickets). There is no mixing of conventions like camelCase, and verbs are predictable (get, list, create, search, submit, etc.).

Tool Count4/5

With 28 tools, the count is slightly on the high side but justified by the broad scope covering a neighborhood directory with multiple features (places, events, movies, menus, bookings, forms, etc.). Each tool serves a clear purpose, though a few could be consolidated.

Completeness4/5

The tool surface is comprehensive for the server's purpose, covering search, details, bookings, events, movies, menus, blogs, FAQs, forms, and more. Minor gaps exist (e.g., no tool to cancel bookings or update reviews), but core workflows are well-covered.

Resources

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