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Centro

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

A4.5/5.0
Behavior5/5

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

No annotations are provided, so the description carries full burden. It discloses accent-insensitive and typo-tolerant matching, filter semantics (ALL must match for attributes), and result fields (open_now, attributes, delivery, rating, structured hours). This level of detail is excellent for an agent.

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 a single paragraph but front-loaded with the core purpose. It contains useful details without redundancy. While it could be slightly more structured, it remains concise and every sentence adds value.

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 the tool has 6 parameters, no required ones, and no output schema, the description covers the matching algorithm, all filters, and key result fields. It lacks error handling or rate limits but is sufficient for a search tool with good schema coverage.

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%, providing a baseline of 3. The description adds value by explaining accent-insensitivity and typo-tolerance for 'q', the meaning of 'open_now' (exact moment), and that 'attributes' must all match. These details go beyond the 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 tool searches for places like restaurants and cafes, and distinguishes from siblings such as search_dishes (searches dishes) and get_place (single place). The verb 'search' and resource 'places' are specific, avoiding tautology.

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 matching behavior and filter composition, implying usage for general place search with filters. While it doesn't explicitly list when not to use or alternatives, the sibling names provide context, and the description is clear enough for typical usage.

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

A4.2/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose. Tools like create_booking and request_reservation target different services (appointments vs. restaurant tables), and reserve_event_tickets vs. rsvp_event differentiate paid holds from free RSVPs. No overlapping functionality that would confuse an agent.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., create_booking, list_events, search_dishes) using lowercase with underscores. No mixing of conventions or irregular verbs.

Tool Count4/5

28 tools cover a broad domain of neighborhood directory services (bookings, events, movies, restaurant menus, forms, etc.). While the count is slightly high, each tool serves a specific function and is justified by the domain scope.

Completeness5/5

The tool set provides comprehensive coverage for a neighborhood directory: search, booking, reservations, event management, menus, reviews, forms, loyalty, and more. No obvious gaps; all core user needs are addressed.

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