# Viral Hook MCP — Hook Formulas for Creators & AI Agents (`elisabethhitz/viral-hook-mcp`) Actor Hook formulas for creators and AI agents. 10 hook styles (contrarian, curiosity gap, pattern interrupt, confession, story open...) as fill-in structures, niche-tuned examples across 10 niches, and a rewrite-as-hook tool that turns any topic into a scroll-stopping opener. - **URL**: https://apify.com/elisabethhitz/viral-hook-mcp.md - **Developed by:** [Elisabeth](https://apify.com/elisabethhitz) (community) - **Categories:** AI, Agents, Social media - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 tool calls This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Viral Hook MCP — Hook Formulas for Creators & AI Agents **The first 2 seconds decide everything. This MCP gives your AI agent the hook mechanics, not guesses.** ### What it does Provide a topic or a draft opener — get back structured hook formulas, niche-tuned examples, and rewrites. Built from patterns that survive across platforms (TikTok, Reels, Shorts, X, LinkedIn), not one-week trend templates. ### 5 Tools | Tool | What it returns | |---|---| | `get_hook_formulas` | The 10 hook styles as fill-in structures with why each works and its failure mode | | `get_hook_examples` | Niche-tuned examples for your niche (10 niches: business, AI/tech, fitness, parenting, marketing, finance, ecommerce, content creation, productivity, lifestyle) | | `rewrite_as_hook` | Turns your topic or draft line into openers across multiple hook styles | | `get_niches` | Lists supported niches | | `get_full_hook_library` | The complete library in one payload for agent context | ### Hook styles covered contrarian · specific_number · curiosity_gap · bold_claim · confession · stop_doing · pattern_interrupt · story_open · question_hook · future_pace ### Pricing Apify pay-per-event: from $0.05 per tool call. First calls free to test. ### Part of a 17-server family Cold outreach, creator deals, deliverability, compliance, and sales intelligence MCPs: [github.com/closermethod](https://github.com/closermethod) · [MCP Hub](https://elisabethhitz-mcp.netlify.app) ### Built by [Elisabeth Hitz](https://www.linkedin.com/in/elisabethhitz) — 10+ years enterprise sales (Deel, Criteo, HBO), reverse-engineered for creators. # Actor input Schema ## `tool` (type: `string`): Which Viral Hook MCP — Hook Formulas for Creators & AI Agents tool to call ## `niche` (type: `string`): Content niche ## `style` (type: `string`): Hook style for rewrite ## `text` (type: `string`): Text to rewrite as a hook (used by rewrite_as_hook) ## Actor input object example ```json { "tool": "get_hook_formulas", "niche": "fitness_health", "style": "contrarian", "text": "Default text" } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "tool": "get_hook_formulas" }; // Run the Actor and wait for it to finish const run = await client.actor("elisabethhitz/viral-hook-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "tool": "get_hook_formulas" } # Run the Actor and wait for it to finish run = client.actor("elisabethhitz/viral-hook-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 '{ "tool": "get_hook_formulas" }' | apify call elisabethhitz/viral-hook-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=elisabethhitz/viral-hook-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Viral Hook MCP — Hook Formulas for Creators & AI Agents", "description": "Hook formulas for creators and AI agents. 10 hook styles (contrarian, curiosity gap, pattern interrupt, confession, story open...) as fill-in structures, niche-tuned examples across 10 niches, and a rewrite-as-hook tool that turns any topic into a scroll-stopping opener.", "version": "0.0", "x-build-id": "pddanNEh4EvkACbUb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/elisabethhitz~viral-hook-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-elisabethhitz-viral-hook-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/elisabethhitz~viral-hook-mcp/runs": { "post": { "operationId": "runs-sync-elisabethhitz-viral-hook-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/elisabethhitz~viral-hook-mcp/run-sync": { "post": { "operationId": "run-sync-elisabethhitz-viral-hook-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "tool" ], "properties": { "tool": { "title": "Tool", "enum": [ "get_hook_formulas", "get_hook_examples", "rewrite_as_hook", "get_niches", "get_full_hook_library" ], "type": "string", "description": "Which Viral Hook MCP — Hook Formulas for Creators & AI Agents tool to call", "default": "get_hook_formulas" }, "niche": { "title": "Niche", "enum": [ "business", "ai_tech", "fitness_health", "parenting", "marketing_sales", "finance_investing", "ecommerce", "content_creation", "productivity", "lifestyle" ], "type": "string", "description": "Content niche", "default": "fitness_health" }, "style": { "title": "Style", "enum": [ "contrarian", "specific_number", "curiosity_gap", "bold_claim", "confession", "stop_doing", "pattern_interrupt", "story_open", "question_hook", "future_pace" ], "type": "string", "description": "Hook style for rewrite", "default": "contrarian" }, "text": { "title": "Text", "type": "string", "description": "Text to rewrite as a hook (used by rewrite_as_hook)", "default": "Default text" } } }, "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 } } } } } } } } } } ```