Agenda SP

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?

With no annotations, the description fully discloses behavioral traits: query matching robustness, filter semantics (open_now server-side, attributes ALL match), and result fields including delivery deep-links and structured hours. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise at ~150 words, every sentence provides useful information. Front-loaded with purpose, followed by details on query behavior, filters, and result fields. No redundancy or waste.

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 thoroughly explains return values (open_now, attributes, delivery, rating, hours) and lists common amenity slugs. Covers all 6 parameters with practical examples, making it complete for an AI agent.

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% but description adds significant value: notes accent-insensitive and typo-tolerant for 'q', server-side computation for 'open_now', and ALL-must-match for 'attributes'. These enrich understanding beyond the schema.

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) in a neighborhood, using a specific verb and resource. It distinguishes from siblings like 'search_dishes' or 'get_place' by its scope and filter capabilities.

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 when to use this tool (free-text and filter-based place search) and details matching behavior (accent-insensitive, typo-tolerant). It implicitly suggests alternatives like 'get_place' for known IDs, but lacks explicit when-not-to-use guidance.

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

A4.1/5.0

Disambiguation4/5

Tools are mostly distinct with clear descriptions. Some overlap in actions that require user to click a link (create_booking, create_checkout, request_reservation, etc.), but each targets a different domain (booking, store, restaurant, event tickets, RSVP). Minor ambiguity possible but descriptions clarify.

Naming Consistency5/5

All tools follow a consistent verb_noun snake_case pattern (e.g., create_booking, get_availability, search_places). No deviations or mixed conventions.

Tool Count4/5

28 tools is slightly high for a neighborhood directory, but each serves a specific function (search, booking, events, movies, offers, etc.). The scope justifies the count, though some tools like list_blog_posts and list_faqs might be considered niche.

Completeness4/5

The tool set covers search, place details, categories, menus, events, movies, offers, booking, and forms. Missing update/delete operations for places or user management, but these are likely out of scope for a read-only directory. Core queries and actions are well-supported.

Resources

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