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Grab_list_areas

Read-only

List the location values GrabFood data uses, with restaurant counts, so you know what to pass as city.

Without city: returns Grab cities (service regions) per country. With city: returns that city's delivery zones (Grab's "suburb" labels, e.g. "Bekasi 3", "JKT.W-KEBONJERUK", "Serpong BSD"); names are inconsistent and about half of restaurants (70% in Indonesia) have none, so prefer the area substring on the search tools for neighbourhoods.

Coverage: ~1M restaurants: Indonesia 711k (164 Grab cities), Malaysia 119k, Vietnam 92k, Philippines 53k, Cambodia 15k, Singapore 4.5k, Myanmar 3k. Menus were fetched between Jun and Sep 2026 (fetchedAt per restaurant).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoReturn this city's delivery zones.
queryNoSubstring on the city or zone name.
countryNoISO country code, uppercase. Only these countries have data.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnlyHint, destructiveHint, openWorldHint), and the description adds substantial non-obvious context the annotations cannot: coverage volumes per country, data-quality caveats (inconsistent zone names, ~half of restaurants lacking an area), and a staleness window ('menus were fetched between Jun and Sep 2026').

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?

Purpose is front-loaded, then parameter behaviour, then caveats and coverage. It is dense and mostly earns its length, though the per-country restaurant counts are closer to trivia than decision-relevant information for selecting or calling the tool.

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?

With three optional parameters and no output schema, the description carries the full burden and does so: it explains both result shapes, the enum's scope ('only these countries have data'), the naming quality caveat, and the data vintage. An agent can call this correctly without further exploration.

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 description coverage is 100%, so 3 is the baseline. The description goes beyond the terse schema text by explaining what each mode returns and by warning that zone names are unreliable ('Bekasi 3', 'JKT.W-KEBONJERUK'), which materially affects how an agent should interpret a city value.

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 opening sentence names a specific verb and resource ('List the location values ... with restaurant counts') and immediately ties it to its downstream use ('so you know what to pass as city'). It also distinguishes itself from the search siblings by naming them as the preferred path for neighbourhood lookups.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit branching behaviour (call without city for Grab cities, with city for delivery zones) and an explicit routing rule away from this tool: 'prefer the area substring on the search tools for neighbourhoods'. That is a when-to-use, when-not-to-use, and alternative in one.

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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