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Foodly

by solutions.foodly
OverviewDiscussionsAdmin

Find restaurants near a place

find_restaurants
Read-onlyIdempotent

Restaurants, bars, cafés and caterers on Foodly near a point, nearest first, optionally matching a dish, cuisine or name. Foodly is new, so there may be few or none.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "latitude": "<latitude>",
      "longitude": "<longitude>"
    },
    "name": "find_restaurants"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryNoA dish, a cuisine or a restaurant name: arepas, sushi, Arepate.
latitudeYesLatitude of the place the diner asks about (a city, a street or where they are).
longitudeYes
radius_kmNoHow far to look, in km. Up to 25.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so safety is covered. The description adds genuinely new behavioral context: results are ordered nearest-first, coverage spans four venue categories, and the catalog may be sparse so empty results are plausible rather than an error. It stops short of stating result caps or pagination behavior.

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?

Two short sentences with no filler: the first front-loads what is returned, ordering, and the optional filter; the second preempts the false-negative interpretation of an empty result. Every clause carries information an agent needs.

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?

For a four-parameter read-only search with no output schema, the description covers scope, ordering, filtering and the sparse-catalog caveat, which is enough to call it correctly. Minor gaps remain around maximum results returned and whether an empty response means 'no matches' versus 'out of area'.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 75% with the query, latitude and radius_km fields already documented, so the schema does most of the work. The description restates the query dimension (dish/cuisine/name) and the point-based location, adding little syntax or constraint detail beyond the schema; longitude remains undocumented in both places.

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?

States a concrete resource set (restaurants, bars, cafés, caterers on Foodly), a spatial scope (near a point), an ordering guarantee (nearest first), and an optional filter dimension (dish, cuisine, name). An agent can immediately tell this is a proximity search, distinct from single-entity siblings like get_restaurant or get_menu.

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?

Usage is implied through 'near a point' plus the optional query filter, and the caveat 'Foodly is new, so there may be few or none' usefully sets expectations about empty results. However, no explicit when-to-use/when-not guidance or named alternative (e.g. get_restaurant for a known venue) is provided, leaving routing to inference.

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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