Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者による説明(英語の原文)
Read-only Transport for London data: line status, arrivals, journeys, stop lookup.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
TFL_APP_KEY, TFL_MCP_MIN_INTERVAL_MS「秘密」と表示された値は、あなたのアカウントの鍵です。絶対に人に教えたり、チャットに貼り付けたりしないでください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"tfl-ts": {
"command": "npx",
"args": [
"-y",
"tfl-ts",
"mcp"
],
"env": {
"TFL_APP_KEY": "your-value",
"TFL_MCP_MIN_INTERVAL_MS": "your-value"
}
}
}
}ターミナルでこれを実行します
claude mcp add tfl-ts --env TFL_APP_KEY=your-value --env TFL_MCP_MIN_INTERVAL_MS=your-value -- npx -y tfl-ts mcp動かす前に、空欄の値を入力してください。
プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"tfl-ts": {
"command": "npx",
"args": [
"-y",
"tfl-ts",
"mcp"
],
"env": {
"TFL_APP_KEY": "your-value",
"TFL_MCP_MIN_INTERVAL_MS": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"tfl-ts": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"tfl-ts",
"mcp"
],
"env": {
"TFL_APP_KEY": "your-value",
"TFL_MCP_MIN_INTERVAL_MS": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
TFL_MCP_MIN_INTERVAL_MS envこの設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント ghcpuman902 で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
入力が必要なもの:TFL_APP_KEY
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(2.15.2)がレジストリに届いたのは22 日前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。
同じカテゴリには:Spec Driven Development, Remote Desktop Commander, 好縮 Short
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Smart URL shortener: create, tag and track short links on s.gooo.name.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
Typed TfL client: friendly wrappers, 84 raw REST endpoints, and offline station topology for 20 rail lines.
Built with tfl-ts → Board
| tfl-ts | tfl-unified-api | tfl-journey-planner | tfl-mcp | mcp-tfl | |
|---|---|---|---|---|---|
| TypeScript types | Yes | No | No | Source only | Yes (src/index.ts) |
| Zero dependencies | Yes | No | No | No | No |
| Raw endpoint coverage | 84 | 10 of 84 | 1 of 84 | 59 of 84 | 8 of 84 |
| Offline station data | Yes | No | No | No | No |
| MCP support | Yes | No | No | Yes | Yes |
Counts use the 84 operations in the committed OpenAPI snapshot (ENDPOINT_COUNT in src/generated/endpoints.ts, pathCount in src/generated/openapi/spec.meta.json). Checked against each repo's source and the package.json published to npm on 16 October 2026.
tfl-ts publishes .d.ts files. Runtime dependencies: none. typescript is a peer dependency, for people compiling against the types. Station order and hubs ship in tfl-ts/meta, with no network call. tfl mcp is the local MCP server.
tfl-unified-api 2.0.2 is JavaScript, with no types field. It depends on moment, request, and request-promise-native. The Line module calls ten of the 84 paths: meta modes, severity, disruption categories, service types, route sequence, route by id, lines by mode, status by id, status over a date range, and status by mode. getRoutesByServiceType builds /Line/Route/{types}, which sits outside those 84 paths. db/lineColors.json is line colours, not stations.
tfl-journey-planner 1.1.2 is LiveScript compiled to JavaScript, with no types field. It depends on request and querystring. The module calls GET /Journey/JourneyResults/{from}/to/{to} only.
@daanrongen/tfl-mcp 1.4.3 is written in TypeScript. The published tarball is dist/main.js, package.json, and README.md, and package.json has no types field. It depends on @modelcontextprotocol/sdk, effect, and zod. Its tools call 59 of the 84 paths. They also call /Road/all/Disruption (the snapshot path is /Road/all/Disruption/{disruptionIds}), /Vehicle/EmissionSurcharge, and /Vehicle/UlezCompliance.
@pipeworx/mcp-tfl 0.1.2 sets "types": "src/index.ts", and that file is in the tarball. It depends on @modelcontextprotocol/sdk and tsx. Six tools call 8 of the 84 paths.
Station order ships with tfl-ts, in tfl-ts/meta.
Get a Primary key from the TfL API Portal. Subscribe to "500 Requests per min", then Profile → Show.
pnpm add tfl-ts
export TFL_APP_KEY=your-primary-key pnpm exec tfl raw line.statusByIds --ids victoria
[
{ "id": "victoria", "name": "Victoria", "lineStatuses": [{ "statusSeverityDescription": "Good Service" }] } ]
Line names, station order, and colours ship in the package. Status, arrivals, and journeys hit TfL at runtime.
On the portal you'll see two keys (Primary and Secondary); either works. app_id has been unused since Jan 2021.
Pass credentials in the constructor instead
const client = new TflClient({
appKey: 'your-primary-key', });
import TflClient from 'tfl-ts';
const client = new TflClient(); // reads TFL_APP_KEY from process.env
const status = await client.line.getStatus({ modes: ['tube'] });
const arrivals = await client.stopPoint.getArrivals({ stopPointIds: ['940GZZLUOXC'], // Oxford Circus });
const journey = await client.journey.plan({ from: '940GZZLUOXC', to: '940GZZLUBND', });
const { matches } = await client.stopPoint.search({ query: 'Oxford Circus', modes: ['tube'], });
const busStops = await client.stopPoint.searchBusStops('Trafalgar Sq');
Server Component with ISR (~60s). Boards and explorer: tfl-components · tfl.manglekuo.com/docs/explorer. Tube boards use official line colours; bus boards use route-number chips. Do not mix. See examples/README.md.
import TflClient, { sortLinesBySeverityAndOrder } from 'tfl-ts';
export const revalidate = 60;
const client = new TflClient(); const tube = await client.line.getStatus({ modes: ['tube'] }); const sorted = sortLinesBySeverityAndOrder(tube);
pnpm dlx shadcn@latest add https://tfl.manglekuo.com/r/tube-status-board.json
pnpm dlx shadcn@latest add https://tfl.manglekuo.com/r/tfl-roundel.json
The roundel ships a placeholder unless NEXT_PUBLIC_ALLOW_TFL_ROUNDEL=true (you accept trademark responsibility).
Read-only MCP (npx tfl-ts mcp), cached and rate-limited. Static tools never call TfL. The npm bin is tfl (dist/cjs/bin/tfl.js); mcp is the stdio subcommand. Setup: docs/mcp.md.
{
"mcpServers": { "tfl-ts": { "command": "npx", "args": ["-y", "tfl-ts@latest", "mcp"], "env": { "TFL_APP_KEY": "your-primary-key" } } } }
Tools: get_supported_modes, resolve_line_id, docs, resolve_stop_id, get_line_status, get_arrivals, plan_journey.
No repo access, or lost your context? npx tfl-ts docs ls lists every bundled agent doc (this README, CLAUDE.md, docs/agent.md, docs/mcp.md, …). docs cat prints one, and docs find / docs grep search across all of them — offline, no API key. The local MCP docs tool reads the same catalogue.
'central', 'victoria', 'elizabeth'. Not display names like 'Central'.'940GZZLUOXC'. Resolve with stopPoint.search() or place.search().'51800').LINE_NAMES, STATION_SEQUENCES, mode lists) before live calls.accidentStats and airQuality are deprecated.STATION_HUBS tracks Southeastern, South Western Railway, and similar operators for topology, but getArrivals() returns an empty array for them, not an error.lineStatuses. Do not read [0]. Use getWorstCurrentStatus(line.lineStatuses) for the operative row.validityPeriods[].isNow follows disruption.category === 'RealTime'. It is not a clock check. Planned engineering can be in force today with isNow: false.validityPeriods[].toDate is the window end, not when trains resume. Weekend engineering often ends at 00:29Z (01:29 London, end of the traffic day). Overnight-split slices on one row are one possession; do not treat the first overlapping toDate as the next train.sortLinesBySeverityAndOrder ranks it after incidents.lineId flips along shared track. Use withSharedTrackIdentity. Do not rewrite raw lineId.getDetailedStatus() returns shorter types and renamed fields (severity, from, closureType, originName) instead of TfL's generated shapes:
const lines = await client.line.getDetailedStatus({
lineIds: ['bakerloo'], dateRange: { startDate: '2026-08-08', endDate: '2026-08-10' }, }); const s = lines[0]?.statuses?.[0]; s?.severity; s?.severityDescription; s?.validityPeriods?.[0]?.from; s?.disruption?.closureType;
Use getStatus({ detail: true }) or client.raw.line.* for exact TfL field names.
Tube, Elizabeth line, DLR, Overground, Tram, and river-bus pier topology. No credentials, no network. Identity, order, and branches only (no status or arrivals). River piers are not in STATION_HUBS — poll the NaptanFerryPort id. Live topology: client.line.getRouteSequence(). Also on the client: client.line.STATION_SEQUENCES.
import { LINE_STATION_SEQUENCES } from 'tfl-ts/meta';
const bakerloo = LINE_STATION_SEQUENCES.bakerloo; const outbound = bakerloo.orderedRoutes.find( (route) => route.direction === 'outbound' && route.serviceType === 'Regular', ); console.log(outbound?.stationIds, bakerloo.branches);
STATION_HUBS maps each physical station to its sibling StopPoint ids and the specific id that carries arrivals for each line — Liverpool Street's tube id (940GZZLULVT) and rail id (910GLIVST) both resolve to one HUBLST entry, with Central on the tube id and Elizabeth line on the rail id. No credentials, no network.
import { STATION_HUBS, resolveArrivalsStopId } from 'tfl-ts/meta';
const hub = STATION_HUBS['940GZZLULVT']; // any sibling id works const elizabethStopId = hub && resolveArrivalsStopId(hub, 'elizabeth'); // '910GLIVST'
resolveArrivalsStopId returns undefined when the hub doesn't carry that line, rather than the interchange id — polling the interchange id itself returns zero arrivals from TfL. Third-party National Rail operators (Southeastern, South Western Railway, c2c, and similar) show up in the hub's topology but never return live predictions: TfL's Arrivals API only covers tube, DLR, tram, Overground, and Elizabeth line.
client.stopPoint.getNormalizedArrivals() is getArrivals() plus a cleaned destination (falls through empty or literal "null" towards to destinationName, common on Elizabeth line, Overground, and some bus termini) and platform (compass bound, cleaned label, isUnknown for TfL's literal "Platform Unknown"):
const arrivals = await client.stopPoint.getNormalizedArrivals({
stopPointIds: ['940GZZLUOXC'], }); arrivals[0]?.destination.name; arrivals[0]?.platform.label;
getArrivals() and client.raw.* are unchanged.
client.stopPoint.get and getByGeoPoint lift Direction additional properties onto the stop: towards, compassPoint, and compassBearingDegrees. smsCode is filled from the first-class field or bag SmsCode. Facility keys stay in additionalProperties as TfL strings. parseAdditionalPropertyValue turns a bag value into null / boolean / number / date / text ("yes", "null", unix ms InstallDate). client.raw.stopPoint.* is unlifted. Do not use Prediction bearing (vehicle heading) as the stop flag; a painted `stopLette
冒頭だけを表示しています。続きはリポジトリで読めます