Aclimação

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

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries full burden and discloses key behaviors: accent-insensitive and typo-tolerant matching, relevance ranking, precomputed open_now, filter semantics, result structure (delivery links, hours format). It does not cover pagination or rate limits, but otherwise thorough.

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?

Description is concise, front-loaded with purpose, and each sentence adds value. No wasted words. Well-structured for quick comprehension.

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?

For a search tool with 6 parameters and no output schema, the description covers query behavior, filters, result fields (open_now, attributes, delivery, rating, hours), and amenity slugs. It lacks pagination details and error handling, but is largely 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%, and description adds meaning beyond schema: explains typo tolerance for 'q', server-side computation for 'open_now', lists common slugs for 'attributes', and mentions case-insensitive substring match for 'category' and 'neighborhood'.

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 like restaurants and cafes in a neighborhood, using a specific verb ('Search') and resource ('places'). It distinguishes itself from sibling tools like 'get_place' (single place) and 'search_dishes' (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?

Usage is implied through description of filters and matching behavior, but there is no explicit guidance on when to use this tool versus alternatives. Given many siblings, lack of when-not-to-use is a gap.

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 domain (booking, events, places, food, movies, etc.) with clear boundaries. Even similar actions like reservations are clearly differentiated by context (booking appointment vs. table vs. event tickets vs. RSVP).

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case (e.g., create_booking, get_availability, list_events, search_places). No mixing of conventions or vague verbs.

Tool Count4/5

28 tools is on the higher end but justifiable for a comprehensive neighborhood directory covering bookings, events, store, reviews, FAQs, guides, offers, movies, and more. Each tool serves a specific purpose without redundancy.

Completeness4/5

The tool surface covers the main user workflows (searching places, booking services, events, food ordering, loyalty, reviews). Minor gaps like lack of user account management or direct payment handling are acceptable for a directory MCP.

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