Filipe Coffee

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

A4.4/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It describes search behavior (accent-insensitive, typo-tolerant), result structure (open_now boolean, attributes, delivery, hours), and notes that open_now is computed server-side. It does not mention pagination details or rate limits, but the core behavioral traits are disclosed.

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 two paragraphs with clear structure: first sentence states purpose, second paragraph dives into details. Every sentence adds value, though some repetition exists (e.g., listing slugs again). It is front-loaded but could be slightly more concise.

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?

No output schema, but the description explains the result fields (open_now, attributes, delivery, hours, rating). It covers all 6 parameters adequately. Missing aspects like sorting/ordering or pagination beyond limit, but overall it is complete for a search tool.

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%, but the description adds crucial context: for `q` it adds accent-insensitive and typo-tolerant; for `open_now` it clarifies 'at this exact moment'; for `attributes` it notes 'ALL must match' and lists common slugs; for `neighborhood` it specifies case-insensitive substring match. This goes well beyond 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 begins with a clear verb ('Search') and resource ('places'), listing examples (restaurants, cafes, shops). This distinguishes it from sibling tools like 'get_place' (single place) and 'search_dishes' (different resource).

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 filter behavior (multi-condition queries with parameters like category, open_now, attributes) and gives details on match algorithm (accent-insensitive, typo-tolerant). However, it does not explicitly contrast with alternative tools for similar tasks (e.g., when to use 'get_place' instead).

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

All Filipe Coffee tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0

Disambiguation5/5

Each tool targets a distinct resource or action (e.g., bookings, events, places, menus, forms), with detailed descriptions that clearly differentiate even similar operations like create_booking vs. reserve_event_tickets. No two tools overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using lowercase and underscores (e.g., create_booking, get_availability, list_events). Verbs are semantically appropriate (create, get, list, search, submit) and predictable.

Tool Count4/5

28 tools is slightly above the typical 3-15 range, but the breadth of the neighborhood directory domain justifies each tool. The count feels dense but not excessive, with no redundant tools.

Completeness5/5

The tool set covers the full lifecycle of a neighborhood directory: place search, restaurant menus, events, movie showtimes, booking, reviews, blog, guides, offers, loyalty, forms, and profiles. Obvious gaps like review creation are absent but likely out of scope.

Resources

Need Help?