MONO 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

A4.5/5.0

Behavior5/5

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

No annotations are provided, so the description bears full burden. It thoroughly discloses matching behavior (accent-insensitive, typo-tolerant, ranked by relevance), filter semantics (open_now server-side, attributes ALL must match), and result structure (open_now boolean, attributes array, delivery deep-links, rating, hours format). 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 dense and well-structured, starting with purpose, then matching details, filters, and result structure. However, it is quite long; could possibly be trimmed slightly without losing key information. Every sentence earns its place, but overall length reduces conciseness.

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?

Given the tool has 6 parameters, no output schema, and no annotations, the description is remarkably complete. It covers all parameter behaviors, matching semantics, filter logic, and the full result structure (including the delivery array format and common amenity slugs). It leaves no significant gaps.

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?

Schema coverage is 100% (baseline 3), but the description adds significant value: explains accent-insensitive/typo-tolerant behavior for q, case-insensitive substring for category, server-side computation for open_now, ALL-match semantics for attributes, and lists common amenity slugs. This far exceeds 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 it searches for places (restaurants, cafes, shops, etc.) in a neighborhood. It specifies the verb 'search' and the resource 'places', distinguishing it from siblings like search_dishes or get_place.

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 when searching for places with filtering, but does not explicitly say when not to use or compare with alternative tools like search_dishes. Lacks explicit when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All MONO Social tools Inspect

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TDQS

A4.1/5.0

Disambiguation5/5

Each tool targets a distinct domain action, with clear boundaries between similar operations (e.g., create_booking vs request_reservation vs reserve_event_tickets). Descriptions provide enough context to avoid confusion.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case, e.g., create_booking, get_availability, list_events. No mixing of styles or ambiguous verbs.

Tool Count4/5

28 tools is slightly above average but appropriate for a multi-feature neighborhood directory covering bookings, events, movies, menus, forms, etc. Each tool serves a distinct purpose, justifying the count.

Completeness4/5

The tool set covers most expected operations for a directory site: search, details, reviews, booking, events, menus, blog, FAQ, forms, etc. Minor gaps like cancellation or user booking history are acceptable given the domain scope.

Resources

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