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Glama

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

A4.1/5.0
Behavior4/5

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

With no annotations, the description fully covers behavioral traits: accent-insensitivity, typo-tolerance, filter semantics (open_now server-side, attributes must all match), and output structure (open_now boolean, attributes array, delivery links, rating, hours format).

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-structured, with front-loaded purpose and detailed explanations. Every sentence adds value, but could be slightly tightened.

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 6 parameters, no required ones, and no output schema, the description thoroughly explains filter behavior and output fields, making it complete for an AI agent to invoke correctly.

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 has 100% coverage, but the description adds significant value by explaining behaviors like typo-tolerance for q, open_now computed locally, and ALL-matching for attributes, plus providing example amenity slugs and output field details.

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 provides specific capabilities (accent-insensitive, typo-tolerant) and filter options, making the purpose distinct from sibling tools.

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 implies usage by detailing filters and behavior, but does not explicitly state when to use this tool versus alternatives like get_place or search_dishes.

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

Every tool has a clearly distinct purpose, with detailed descriptions that prevent ambiguity. Overlaps like create_booking vs request_reservation are clarified by domain (appointments vs restaurant) and mechanics.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., create_booking, list_events, search_places). The same conjugation style (snake_case) is used throughout, with no mixing of conventions.

Tool Count4/5

At 28 tools, the set is slightly larger than the typical well-scoped range (3-15), but each tool corresponds to a distinct feature of the neighborhood directory, making the count reasonable for the breadth of functionality.

Completeness5/5

The tool surface covers all major user-facing actions: searching, browsing, booking, purchasing, and retrieving information. No obvious gaps exist for the stated purpose of a neighborhood directory.

Resources