Genesis Social

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

A4.7/5.0

Behavior5/5

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

Since no annotations are provided, the fully detailed description covers query behavior (accent-insensitive, typo-tolerant, relevance-ranked), filter semantics, and result fields (open_now, attributes, delivery links, rating, hours).

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 a single paragraph but well-structured, front-loaded with purpose, and every sentence adds essential information without 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?

For a search tool with 6 parameters, no output schema, and no annotations, the description covers all parameters, result structure, and edge cases (e.g., open_now semantics, amenity slugs), leaving no critical gaps.

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 the description adds value beyond schema by explaining typo-tolerance, accent-insensitivity, open_now calculation, and providing amenity slug examples.

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 uses a specific verb 'Search for' and lists example place types (restaurants, cafes, shops), clearly distinguishing it from sibling tools like get_place, search_dishes, and list_categories.

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 how to compose multi-condition queries with filters but does not explicitly state when not to use this tool. However, the sibling tool list provides context for alternatives.

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 targets a distinct action and domain, with clear descriptions preventing confusion. Similar tools like create_booking, request_reservation, reserve_event_tickets, and rsvp_event are differentiated by service type and workflow.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case (e.g., create_booking, get_availability, list_categories), ensuring predictability and ease of use for agents.

Tool Count4/5

With 28 tools, the set is fairly large but appropriate for a comprehensive neighborhood directory covering multiple domains (places, events, booking, store, forms, etc.). Each tool serves a distinct purpose without redundancy.

Completeness5/5

The tool surface covers the full lifecycle of common user needs: search and discovery, detailed info, reservations, purchases, and interactions (forms, FAQs). No obvious gaps for the described purpose of a neighborhood directory.

Resources

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