Get full details for a specific place by its URL slug, including opening hours, a precomputed open_now boolean, amenity attributes (slugs), a delivery array of curated delivery-app deep-links (ifood, rappi, uber-eats — each { platform, url }), ratings, address, and neighborhood.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | URL slug of the place, e.g. "cafe-example". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the full burden. It thoroughly lists the returned data (opening hours, open_now, attributes, delivery links, ratings, address, neighborhood). However, it does not mention error handling, rate limits, or idempotency, which are minor gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the purpose and efficiently enumerates the returned details. No superfluous words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval tool with one parameter and no output schema, the description provides sufficient context about what the tool does and what it returns. It is complete given the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema documentation covers 100% of the single parameter 'slug' with a clear description and example. The description reinforces the purpose of the parameter, adding no new semantic details beyond the schema, but the example adds practical clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves full details for a specific place by its URL slug, listing the returned fields. It effectively distinguishes from sibling tools like search_places, which is for searching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a slug is known, but it does not explicitly mention when to use this tool versus alternatives (e.g., search_places). No exclusions or prerequisites are stated.
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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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.
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.
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.
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.