# Anonymous Instagram Story Viewer (`instagram-story-viewer/instagram-story-viewer-api`) Actor One Of The Fastest Anonymous Instagram Story Viewer API In The World. - **URL**: https://apify.com/instagram-story-viewer/instagram-story-viewer-api.md - **Developed by:** [Instagram Story](https://apify.com/instagram-story-viewer) (community) - **Categories:** AI, Social media, Developer tools - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ### MCP server template A template for creating a [Model Context Protocol](https://modelcontextprotocol.io) server using [Streamable HTTP transport](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#streamable-http) on [Apify platform](https://docs.apify.com/platform). This template includes a simple example MCP server with: - An `add` tool that adds two numbers together with structured output - A dummy `calculator-info` resource endpoint - Pay [Per Event monetization](https://docs.apify.com/platform/actors/publishing/monetize#pay-per-event-pricing-model) support ### How to use 1. **Modify the server**: Edit `src/main.ts` to add your own tools and resources 2. **Add new tools**: Use the `server.registerTool()` method to register new tools 3. **Add new resources**: Use the `server.registerResource()` method to register new resources 4. **Update billing**: Configure billing events in `.actor/pay_per_event.json` and charge for tool calls The server runs on port 3000 (or APIFY_CONTAINER_PORT if set) and exposes the MCP protocol at the `/mcp` endpoint. ### Running locally ```bash npm install npm run start:dev ```` The server will start and listen for MCP requests at `http://localhost:3000/mcp` ### Deploying to Apify [Push your Actor](https://docs.apify.com/academy/deploying-your-code/deploying) to the Apify platform and configure [standby mode](https://docs.apify.com/platform/actors/development/programming-interface/standby). Then connect to the Actor endpoint with your MCP client: `https://me--my-mcp-server.apify.actor/mcp` using the [Streamable HTTP transport](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#streamable-http). **Important:** When connecting to your deployed MCP server, pass your Apify API token in the `Authorization` header as a Bearer token: ``` Authorization: Bearer ``` #### Pay per event This template uses the [Pay Per Event (PPE)](https://docs.apify.com/platform/actors/publishing/monetize#pay-per-event-pricing-model) monetization model, which provides flexible pricing based on defined events. To charge users, define events in JSON format and save them on the Apify platform. Here is an example schema with the `tool-call` event: ```json [ { "tool-call": { "eventTitle": "Price for completing a tool call", "eventDescription": "Flat fee for completing a tool call.", "eventPriceUsd": 0.05 } } ] ``` In the Actor, trigger the event with: ```typescript await Actor.charge({ eventName: 'tool-call' }); ``` This approach allows you to programmatically charge users directly from your Actor, covering the costs of execution and related services. To set up the PPE model for this Actor: - **Configure Pay Per Event**: establish the Pay Per Event pricing schema in the Actor's **Monetization settings**. First, set the **Pricing model** to `Pay per event` and add the schema. An example schema can be found in [pay\_per\_event.json](.actor/pay_per_event.json). ### Resources - [What is Anthropic's Model Context Protocol?](https://blog.apify.com/what-is-model-context-protocol/) - [How to use MCP with Apify Actors](https://blog.apify.com/how-to-use-mcp/) - [TypeScript MCP SDK examples](https://github.com/modelcontextprotocol/typescript-sdk/tree/main) - [TypeScript tutorials in Academy](https://docs.apify.com/academy/node-js) - [Apify SDK documentation](https://docs.apify.com/sdk/js/) - [Webinar: Building and monetizing MCP servers on Apify](https://www.youtube.com/watch?v=w3AH3jIrXXo) - [Apify MCP server documentation](https://docs.apify.com/platform/integrations/mcp) - [Apify MCP server configuration](https://mcp.apify.com/) ### Getting started For complete information [see this article](https://docs.apify.com/platform/actors/development#build-actor-at-apify-console). In short, you will: 1. Build the Actor 2. Run the Actor ### Pull the Actor for local development If you would like to develop locally, you can pull the existing Actor from Apify console using Apify CLI: 1. Install `apify-cli` **Using Homebrew** ```bash brew install apify-cli ``` **Using NPM** ```bash npm -g install apify-cli ``` 2. Pull the Actor by its unique ``, which is one of the following: - unique name of the Actor to pull (e.g. "apify/hello-world") - or ID of the Actor to pull (e.g. "E2jjCZBezvAZnX8Rb") You can find both by clicking on the Actor title at the top of the page, which will open a modal containing both Actor unique name and Actor ID. This command will copy the Actor into the current directory on your local machine. ```bash apify pull ``` ### Documentation reference To learn more about Apify and Actors, take a look at the following resources: - [Apify SDK for JavaScript documentation](https://docs.apify.com/sdk/js) - [Apify SDK for Python documentation](https://docs.apify.com/sdk/python) - [Apify Platform documentation](https://docs.apify.com/platform) - [Join our developer community on Discord](https://discord.com/invite/jyEM2PRvMU) # Actor input Schema ## Actor input object example ```json {} ``` # 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("instagram-story-viewer/instagram-story-viewer-api").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("instagram-story-viewer/instagram-story-viewer-api").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 instagram-story-viewer/instagram-story-viewer-api --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=instagram-story-viewer/instagram-story-viewer-api", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Anonymous Instagram Story Viewer", "description": "One Of The Fastest Anonymous Instagram Story Viewer API In The World.", "version": "0.0", "x-build-id": "z50l8Pqv5rEBDoVBU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/instagram-story-viewer~instagram-story-viewer-api/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-instagram-story-viewer-instagram-story-viewer-api", "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/instagram-story-viewer~instagram-story-viewer-api/runs": { "post": { "operationId": "runs-sync-instagram-story-viewer-instagram-story-viewer-api", "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/instagram-story-viewer~instagram-story-viewer-api/run-sync": { "post": { "operationId": "run-sync-instagram-story-viewer-instagram-story-viewer-api", "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": {} }, "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 } } } } } } } } } } ```