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Get Stops Around Location

get_stops_around_location
Read-onlyIdempotent

Discovers transit stops near a geographic point, returning each stop's numeric code, name, coordinates, and walking distance. Also emits a map UI block with multiple markers for map-capable clients (e.g. ChatGPT). Use this as the first step whenever the user provides an address, place name, or coordinates and you need stop IDs before calling get_stop_realtime or get_stop_geometry. Do NOT use this to fetch arrivals or live vehicle data — it returns stop metadata only. Default radius is 1 000 m; narrow it (e.g. 300 m) for dense urban areas or widen it (up to 3 000 m) for rural locations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latitudeYesDecimal latitude of the search centre, WGS84 (e.g. 49.842 for central Lviv).
longitudeYesDecimal longitude of the search centre, WGS84 (e.g. 24.031 for central Lviv).
radius_metersNoSearch radius in metres (50–3000, default 1000). Use ~300 for dense urban intersections, up to 3000 for suburban or rural areas.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
viewYes
ui_blocksYes

TDQS

A4.7/5.0
Behavior5/5

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

Annotations provide readOnlyHint, openWorldHint, idempotentHint, but the description goes further by disclosing the map UI side effect for map-capable clients, the metadata-only nature of the response, and the default radius behavior. These are meaningful behavioral insights beyond the structured annotations.

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?

The description is efficiently front-loaded with the core purpose, followed by the map UI detail, usage guidance, exclusions, and radius advice. Five sentences, each with a distinct purpose, no fluff or redundancy.

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?

For a tool with 3 parameters and an output schema, the description fully covers the tool's purpose, return value characteristics, side effects, usage context, and parameter adjustment tips. It is sufficiently complete to guide an agent without needing additional documentation.

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 description coverage is 100%, so the baseline is 3. The description reinforces the radius guidance already present in the schema (default 1,000 m, narrowing/widening) but doesn't add substantially new parameter-level meaning. It mentions output fields, but not param syntax beyond what schema covers.

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 opens with a specific verb ('Discovers') and clearly defines the resource ('transit stops near a geographic point') and the output (numeric code, name, coordinates, walking distance). It explicitly distinguishes the tool from siblings by adding 'Do NOT use this to fetch arrivals or live vehicle data — it returns stop metadata only.'

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?

The description gives explicit usage guidance: 'Use this as the **first step** whenever the user provides an address, place name, or coordinates and you need stop IDs before calling get_stop_realtime or get_stop_geometry.' It also clearly states what not to use it for, making the when/when-not boundaries unambiguous.

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

A4.7/5.0
Disambiguation5/5

Each tool targets a distinct resource (route, stop, vehicle, nearby) and mode (static vs. realtime), with clear cross-references that prevent overlap. The descriptions explicitly state when to prefer one tool over another, eliminating ambiguity.

Naming Consistency5/5

All tool names follow a strict get_<resource>_<modifier> pattern (e.g., get_route_realtime, get_stop_geometry), making the API predictable. Verb and noun usage is uniform throughout the set.

Tool Count5/5

Seven tools is a well-scoped size for a transit data server, covering static and realtime data for routes, stops, and vehicles. Each tool serves a necessary purpose with no redundancy.

Completeness4/5

Core workflows (discover stops by location, get route and stop data, track vehicles) are well covered. The only notable gap is lack of stop search by name; get_stops_around_location requires coordinates, so a stop name alone cannot resolve without external geocoding.