# DOT Transit Intelligence MCP (`andrew_avina/dot-transit-mcp`) Actor Access DOT National Transit Database (NTD) ridership and finance data via MCP. Returns annual ridership (unlinked passenger trips), vehicle revenue miles, transit mode, and agency information for US transit systems. - **URL**: https://apify.com/andrew\_avina/dot-transit-mcp.md - **Developed by:** [Andrew Avina](https://apify.com/andrew_avina) (community) - **Categories:** MCP servers, Business - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $3.00 / 1,000 result item returneds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## DOT Transit Intelligence MCP Access DOT National Transit Database (NTD) ridership and finance data via MCP. Returns annual ridership (unlinked passenger trips), vehicle revenue miles, transit mode, and agency information for US transit systems. ### Data Source - **API**: DOT National Transit Database via Data.gov Socrata - **Ridership**: `https://data.transportation.gov/resource/y9gs-7uf6.json` - **Agencies**: `https://data.transportation.gov/resource/9qjj-6r5p.json` - **Coverage**: 800+ US transit agencies, annual data ### MCP Tools | Tool | Description | |---|---| | `get_transit_agencies` | Retrieve NTD data filtered by agency, state, mode, year | | `get_transit_summary` | Aggregate ridership stats: total, by mode, top agencies | | `list_major_transit_agencies` | Always-available list of 20 largest US transit agencies | | `compare_transit_agencies` | Side-by-side comparison of multiple agencies | ### Input Parameters | Parameter | Type | Description | |---|---|---| | `serveMcp` | boolean | Start HTTP MCP server on port 4321 | | `agencyName` | string | Partial agency name (e.g. "NYC Transit", "BART", "CTA") | | `state` | string | 2-letter state abbreviation (NY, CA, IL...) | | `mode` | string | "bus", "rail", "subway", "light_rail", or "all" (default) | | `year` | integer | Data year (default 2022) | | `limit` | integer | Max records (1–200, default 50) | ### Output Fields | Field | Description | |---|---| | `agency_name` | Full transit agency name | | `city` | City | | `state` | State abbreviation | | `mode` | Transit mode (bus/rail/subway/light_rail/...) | | `annual_ridership` | Annual unlinked passenger trips (UPT) | | `vehicle_revenue_miles` | Annual vehicle revenue miles (VRM) | | `year` | Data year | | `urban_area` | NTD urbanized area name | | `source` | Data source URL | ### Transit Modes | Code | Mode | |---|---| | HR | Subway (Heavy Rail) | | LR | Light Rail | | MB | Bus | | CR | Commuter Rail | | RB | Bus Rapid Transit | | CB | Commuter Bus | | SR | Streetcar | ### Fallback Data (2022 NTD) 20 major transit agencies: NYC Transit (subway + bus), Chicago CTA (rail + bus), LA Metro (bus + light rail), BART, Boston MBTA (subway + bus), DC Metro, SEPTA (subway + bus), SF Muni, Houston Metro, Dallas DART (light rail + bus), Atlanta MARTA, Denver RTD, Minneapolis Metro Transit, Portland TriMet. ### MCP Server Mode Set `serveMcp: true` to start an HTTP MCP server on port 4321. Point your MCP client at: - `GET /mcp/tools` — list available tools - `POST /mcp/call` — call a tool with `{"name": "...", "arguments": {...}}` # Actor input Schema ## `serveMcp` (type: `boolean`): If true, starts an HTTP MCP server on port 4321 to serve tools to MCP clients instead of running a one-shot batch query. ## `agencyName` (type: `string`): Filter by transit agency name (partial match). Example: 'NYC Transit', 'BART', 'CTA'. ## `state` (type: `string`): Filter by 2-letter US state abbreviation (e.g. 'NY', 'CA', 'IL'). ## `mode` (type: `string`): Filter by transit mode: 'bus', 'rail', 'subway', 'light_rail', or 'all'. ## `year` (type: `integer`): Data year to retrieve (e.g. 2022). ## `limit` (type: `integer`): Maximum number of agency records to return (1–200). ## Actor input object example ```json { "serveMcp": false, "mode": "all", "year": 2022, "limit": 50 } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("andrew_avina/dot-transit-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("andrew_avina/dot-transit-mcp").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call andrew_avina/dot-transit-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=andrew_avina/dot-transit-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "DOT Transit Intelligence MCP", "description": "Access DOT National Transit Database (NTD) ridership and finance data via MCP. Returns annual ridership (unlinked passenger trips), vehicle revenue miles, transit mode, and agency information for US transit systems.", "version": "0.1", "x-build-id": "GiTFWTW2sCUbebA3H" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/andrew_avina~dot-transit-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-andrew_avina-dot-transit-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/andrew_avina~dot-transit-mcp/runs": { "post": { "operationId": "runs-sync-andrew_avina-dot-transit-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/andrew_avina~dot-transit-mcp/run-sync": { "post": { "operationId": "run-sync-andrew_avina-dot-transit-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "serveMcp": { "title": "Serve as MCP Server", "type": "boolean", "description": "If true, starts an HTTP MCP server on port 4321 to serve tools to MCP clients instead of running a one-shot batch query.", "default": false }, "agencyName": { "title": "Agency Name", "type": "string", "description": "Filter by transit agency name (partial match). Example: 'NYC Transit', 'BART', 'CTA'." }, "state": { "title": "State", "type": "string", "description": "Filter by 2-letter US state abbreviation (e.g. 'NY', 'CA', 'IL')." }, "mode": { "title": "Transit Mode", "type": "string", "description": "Filter by transit mode: 'bus', 'rail', 'subway', 'light_rail', or 'all'.", "default": "all" }, "year": { "title": "Year", "type": "integer", "description": "Data year to retrieve (e.g. 2022).", "default": 2022 }, "limit": { "title": "Result Limit", "type": "integer", "description": "Maximum number of agency records to return (1–200).", "default": 50 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```