Bogeyland

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?

Given no annotations, the description fully covers behavior: fuzzy matching, real-time open_now computation, precomputed fields, and structured result details. No contradictions.

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 detailed yet focused, each sentence adds value. It could be slightly more concise but remains well-structured and front-loaded.

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?

Despite no output schema, the description explains all result fields (open_now, attributes, delivery, rating, hours) and common amenity slugs, making it fully complete for agent usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds significant meaning beyond schema: q is typo-tolerant, open_now means exact moment, attributes must ALL match, neighborhood substring match, and explains result structure like delivery links and hours format.

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 it searches for places like restaurants, cafes, shops in a neighborhood, distinguishing it from siblings like 'get_place' (single place) or '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 Guidelines4/5

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

It explains how to use filters and query matching (accent-insensitive, typo-tolerant), and implies context of neighborhood. However, it doesn't explicitly state when not to use this tool vs 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

A4/5.0

Disambiguation5/5

Each tool targets a distinct domain (e.g., booking, restaurant reservation, event RSVP, movie showtimes) with clear descriptions that prevent confusion. Overlapping verbs like 'create' are used for different resource types, and the descriptions explicitly distinguish their contexts.

Naming Consistency5/5

All 28 tools follow a strict verb_noun pattern with underscores, e.g., create_booking, get_availability, list_events. No mixing of conventions or inconsistent verb styles.

Tool Count4/5

28 tools is at the upper end of reasonable for a broad neighborhood directory server covering places, bookings, events, movies, store, forms, and more. While each tool serves a distinct purpose, the count feels slightly high for a single server.

Completeness3/5

The tool surface covers core discovery and booking initiation (search, detail, book, reserve), but lacks lifecycle operations like cancel, update, or list user's existing bookings. Several operations require external checkout or confirmation links, creating workflow gaps.

Resources

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