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Campo Belo

by io.github.junoventures

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
NameRequiredDescriptionDefault
qNoFree-text term matched against place name and address — accent-insensitive and typo-tolerant.
limitNoMax results to return (default 20, max 100).
categoryNoFilter by category label (e.g. "Café"). Case-insensitive substring match.
open_nowNoWhen true, return only places open at the current local time (computed server-side from their opening hours).
attributesNoAmenity 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.
neighborhoodNoFilter by neighborhood name. Case-insensitive substring match.

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and details key behaviors: accent-insensitive and typo-tolerant matching, ranking by relevance, filter semantics (open_now computed server-side, attributes ALL must match), result structure (open_now boolean, attributes array, delivery links, hours). This is thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is moderately long but well-organized: opens with purpose, then details search behavior, then filters, then result structure. Every sentence adds information; no redundancy. Could be slightly more concise on result details but still efficient.

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 no output schema, the description sufficiently explains the return format (open_now, attributes, delivery, rating, hours). All 6 parameters are covered. Complexity is moderate, and the description provides adequate completeness for an agent to understand inputs and outputs.

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%, so baseline is 3. The description adds value beyond schema: e.g., 'q' is typo-tolerant, 'open_now' computed server-side, 'attributes' ALL must match, and common amenity slugs. This enriches understanding beyond the schema's type/description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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, specifying the verb and resource. It distinguishes from siblings like 'get_place' and 'search_dishes' implicitly through scope, but doesn't explicitly differentiate.

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 explains the tool's functionality and filters, but doesn't explicitly state when to use this tool vs. siblings like 'get_place' for specific details or 'search_dishes' for dish-level queries. Usage context is implied but not guided.

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 action on a specific resource (e.g., create_booking vs create_checkout vs reserve_event_tickets). Descriptions clearly differentiate purposes, and no two tools overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., get_availability, list_categories, search_dishes). Verbs like get, list, create, request, rsvp are used uniformly, making the set predictable.

Tool Count4/5

28 tools is above the typical 3-15 range, but the server covers a broad domain (places, events, movies, booking, store, etc.). Each tool serves a clear purpose, so the count is justified for the scope.

Completeness4/5

The tool surface is extensive, covering searching, details, reviews, booking, events, menus, etc. Minor gaps exist (e.g., no cancel/update operations), but they are likely intentional given the user-facing nature of the server.

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