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Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
IDFM_API_KEYYesYour PRIM API key from prim.iledefrance-mobilites.fr. Required for the server to authenticate with the Île-de-France Mobilités PRIM API. Can be provided via the MCP server env configuration or a .env file.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

Real-time public transport for Paris and Île-de-France (Metro, RER, Transilien, Tram, Bus). Typical flow: search_stop to get a station ID, then next_departures with that ID. plan_journey and traffic_disruptions accept plain names directly. All times are Europe/Paris local time.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}

Tools

Functions exposed to the LLM to take actions

NameDescription
search_stopA

Find Paris / Île-de-France stations and stops by name and return their IDs.

Use this first when the user names a station (e.g. "Gentilly", "Châtelet",
"Gare de Lyon") and you need its ID for next_departures. Returns up to 5 matches
with the lines serving each one, so you can pick the right station.

Args:
    query: Station or stop name, in French or English spelling. e.g. "Gentilly".
next_departuresA

Get the next real-time departures at a station, with minutes until departure.

Use for questions like "When is the next RER B from Gentilly?" or "Next metro at
Bastille?". Shows line, destination, minutes to wait, delay and platform.

Args:
    stop_id: Station ID from search_stop (e.g. "stop_area:IDFM:63244"). A plain
        station name also works; the best match is used.
    line: Optional line filter, e.g. "RER B", "Metro 7", "Tram T3a", "Bus 57".
        Leave empty to get all lines at the station.
plan_journeyA

Plan a public transport journey between two places in Île-de-France.

Use for "How do I get from X to Y?" or "Fastest way to Orly from Bastille at 18:00?".
Returns up to 3 options with total duration, number of transfers and each step
(lines to take, where to change, walking time).

Args:
    from_place: Origin: station name, address or landmark (e.g. "Gentilly",
        "Tour Eiffel", "10 rue de Rivoli Paris") or a stop ID from search_stop.
    to_place: Destination, same formats as from_place.
    departure_time: Optional, Paris local time: "18:30" (today) or
        "2026-11-05T18:30". Defaults to now.
traffic_disruptionsA

Get current traffic disruptions, incidents and works on the Île-de-France network.

Use for "Are there problems on Metro 7?", "Is the RER A running?" or "Any
disruptions right now?". Returns active and planned disruptions, most severe first.
Elevator/escalator outages are counted but not listed.

Args:
    line: Optional line, e.g. "Metro 7", "RER B", "Tram T2", "Transilien L".
        Leave empty for a network-wide summary.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A4.3/5.0

Scored across 4 tools

Disambiguation5/5

Each tool maps to a clearly distinct transit task: station lookup, real-time departures, journey planning, and disruption status. There is little risk of misselection, and descriptions reinforce the boundaries between tools.

Naming Consistency3/5

Two tools follow a verb_noun pattern (search_stop, plan_journey), but next_departures is an adjective_noun phrase and traffic_disruptions is a noun_noun phrase. The names are readable and individually clear, but the set does not follow one predictable convention.

Tool Count5/5

Four tools is well-scoped for a focused Île-de-France transit query server. Each tool serves a distinct core user need, and there are no redundant or filler tools.

Completeness4/5

The server covers the main transit workflows: finding stops, checking next departures, planning journeys, and viewing disruptions. Minor gaps remain, such as line-level details, fare information, or accessibility-specific queries, but agents can handle core tasks without dead ends.

Maintenance

ActivityMaintained
ResponsivenessSyncing