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by dev.infranode

Transit Departures

transit_departures
Read-onlyIdempotent

Get live public-transport departures with real-time delays for a stop.

Sourced from GTFS-RT/HVV/VGN. Unlike the static stop list (get_city_resource(slug, resource='transit')), this returns minute-fresh departures including delay for ONE stop. A stop_id is required: fetch the city's transit stops first to discover valid stop IDs, then pass one here. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesCity identifier, e.g. 'berlin' or 'hamburg'. Resolved leniently: the German name with or without umlauts, any casing, a common English exonym or short form also works (München/munich/munchen -> muenchen, cologne -> koeln, frankfurt -> frankfurt-am-main). An unknown name returns 404 with a 'Meintest du ...?' suggestion. list_cities gives the canonical slugs.
stop_idNoRequired stop ID to fetch departures for. Discover a city's stop IDs with get_city_resource(slug, resource='transit') first (each stop carries its id). Format: DELFI 'de:<AGS>:<id>' or a numeric gtfs.de stop id. NOTE: this is NOT the trip_stop_id (nor its deprecated alias stop_id) from station_departures/station_arrivals, whose value identifies one stop of one train run (e.g. '-1203677609210685804-2607251113-13'); passing it returns no_data with a corrective note instead of departures.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
metaYes

TDQS

A5/5.0
Behavior5/5

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

Beyond annotations (readOnly, destructive, idempotent), the description adds: data source (GTFS-RT/HVV/VGN), clarifies it returns minute-fresh data, and warns about incorrect stop_id formats (not trip_stop_id). This adds valuable behavioral context beyond what annotations already declare.

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?

Three concise sentences that front-load purpose, then provide context and usage. Every sentence adds value with no 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?

Given an output schema exists (so return values are covered), the description thoroughly explains the tool's purpose, data source, prerequisite steps, and common pitfalls. Sibling tools are provided for differentiation.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds critical context: stop_id is required despite default null, explains how to obtain valid stop IDs, and includes a detailed note on what NOT to pass (trip_stop_id). This goes beyond the schema's own descriptions.

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 'Get live public-transport departures with real-time delays for a stop', specifying the verb (get), resource (departures), and scope (one stop). It distinguishes itself from siblings like `get_city_resource(resource='transit')` by contrasting static vs live data.

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?

Explicitly tells when to use this tool vs. the static stop list via `get_city_resource`. It also provides a prerequisite: 'fetch the city's transit stops first to discover valid stop IDs, then pass one here.' No guidance on when not to use, but the sibling list includes other journey tools, and the description clearly sets scope.

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

A4.1/5.0
Disambiguation3/5

Most tools have distinct purposes, but there is potential confusion between 'get_city_resource' and specific resource accessors like 'weather', 'air_quality', 'transit_departures', 'station_board_arrivals', and 'station_board_departures'. The descriptions do clarify that some specific tools exist for convenience or source-specific details, but the boundary isn't always sharp. Also, 'compare' could overlap with using 'get_city_resource' repeatedly. An agent might hesitate between using the generic resource accessor and the named tool.

Naming Consistency4/5

The naming convention is mostly consistent: verbs like 'get', 'list', 'compare', and 'pois' (abbreviation) are used. The pattern is generally 'verb_noun' (e.g., 'get_city', 'list_cities', 'station_board_arrivals'). Minor deviations include 'pois' being an acronym rather than a full verb phrase, and 'air_quality' vs 'weather' implying a noun rather than an action. But overall it's predictable and readable.

Tool Count5/5

12 tools is a well-scoped count for a city data platform. The function set covers discovery (list_cities, sources), overview (get_city_overview), base data (get_city), specific data types (weather, air_quality, transit_departures, station_board_*, pois), a generic accessor (get_city_resource), and a comparison tool (compare). Each tool feels necessary and the set is not overwhelming.

Completeness4/5

The tool surface is quite comprehensive for a read-only city information server. It provides discovery (list_cities, get_city_overview), base data, and access to 81 data types via get_city_resource. The named tools cover the most common queries (weather, air quality, transit). A minor gap is the lack of a tool to aggregate or search across cities (though 'compare' helps). Editing or write operations are not expected here, but for read-only, it's nearly complete.

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