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Plan a public transport trip

plan_trip

Plan a point-to-point public transport journey in Norway (Entur JourneyPlanner). Give each endpoint either as a Stop Place ID or as coordinates. Optionally set a departure or arrival time, filter transport modes, and set walk speed. Returns trip patterns with legs, modes, durations, and realtime times.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
to_placeNoDestination Stop Place ID
arrive_byNoIf true, date_time is the desired arrival time instead of departure time
date_timeNoISO 8601 time, e.g. '2026-08-24T09:00:00+02:00'. Defaults to now.
from_placeNoOrigin Stop Place ID (from resolve_stop_place)
walk_speedNoWalking speed in metres per second (default ~1.3)
to_latitudeNoLatitude in decimal degrees (WGS84)
to_longitudeNoLongitude in decimal degrees (WGS84)
from_latitudeNoLatitude in decimal degrees (WGS84)
from_longitudeNoLongitude in decimal degrees (WGS84)
transport_modesNoRestrict to these transit modes, e.g. ['bus','tram']. Omit for all modes.
num_trip_patternsNoHow many alternative trips to return

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It explains that the tool returns trip patterns with legs, modes, durations, and realtime times, which goes beyond the schema. It does not disclose every edge case, but for a planning tool the behavior is sufficiently transparent.

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 compact and front-loaded: purpose first, then endpoint requirements, optional parameters, and output. Every sentence adds useful information with no repetition of schema details.

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?

Given 11 parameters and no output schema, the description does a good job covering the essential behavior and return contents. It could mention how to obtain Stop Place IDs or what happens if both ID and coordinates are supplied, but the core information needed to call the tool correctly is present.

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 coverage is 100%, so the baseline is 3. The description adds meaningful parameter semantics by explaining that each endpoint can be either a Stop Place ID or coordinates, which is not obvious from the flat schema alone. It also summarizes the optional time, mode, and walk speed parameters.

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 uses a specific verb ('Plan'), a clear resource ('point-to-point public transport journey in Norway'), and names the underlying system (Entur JourneyPlanner). This distinguishes it from siblings like get_departures or find_stops_near, which handle different transit lookup tasks.

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 clearly communicates the core usage context: point-to-point journey planning with endpoint and optional constraints. It does not explicitly name alternatives or exclusions, but the context signals from sibling tools make the intended use obvious 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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