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maxim75

TfNSW Trip Planner MCP Server

by maxim75
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

find_nearby

Locate nearby transit stops and points of interest using GPS coordinates. Specify radius and result limits to refine your search, with each result showing distance from your location.

Instructions

Find stops and points of interest near a GPS coordinate.

Each result carries its distance in metres from the coordinate. A dense area
can return hundreds of locations within 500m, so results are capped:
`count` is the true total and `returned` is how many are included. Narrow
`radius_m` rather than raising `max_results` to get more relevant results.

Args:
    latitude: Latitude in decimal degrees, e.g. -33.8613.
    longitude: Longitude in decimal degrees, e.g. 151.2107.
    radius_m: Search radius in metres.
    type_1: TfNSW result category. "GIS_POINT" covers stops and POIs.
    draw_class: Optional TfNSW sub-category filter.
    max_results: Maximum locations to return.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
type_1NoGIS_POINT
latitudeYes
radius_mNo
longitudeYes
draw_classNo
max_resultsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

The description explains that results are capped, with count representing the true total and returned the included count. It also recommends adjusting radius_m over max_results, giving insight into expected behavior. Since no annotations are provided, this textual description is the sole source and is fairly informative.

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 concise but includes necessary behavioral details and a clear parameter list. It is well-organized, with the main purpose first, then capping explanation, then parameter descriptions.

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?

The description covers the tool's purpose, key behaviors (capping, distance in results), and parameter meanings. It does not mention error handling or authentication, but for a simple geospatial search tool this is sufficient. The output schema is referenced indirectly via count and returned.

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?

The description includes an Args section that gives meaning to all parameters: latitude/longitude as coordinates, radius_m as search radius, type_1 as TfNSW category, draw_class as optional sub-category filter, and max_results as maximum returned items. This adds value beyond the schema, which only has types and defaults.

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 clearly states it finds stops and points of interest near a GPS coordinate, which distinguishes it from sibling tools like find_stop (likely by name) and find_stop_by_id (by ID).

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?

It provides usage guidance by advising to narrow radius_m rather than raising max_results for better relevance, and explains the result capping behavior. It does not explicitly contrast with all siblings, but the purpose is clear enough.

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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