# MCP: TikTok Research / Content Intel - Trends & Creators (`seibs.co/mcp-tiktok-research-intel`) Actor MCP server for tiktok-research-intel. AI-agent tools: hashtag\_trend, keyword\_trend, creator\_metrics, video\_engagement, comment\_sentiment, and sound\_trend - the research-grade TikTok datasets the gated Research API gives academics only. x402 and Skyfire ready. For brands and analysts. - **URL**: https://apify.com/seibs.co/mcp-tiktok-research-intel.md - **Developed by:** [Seibs.co](https://apify.com/seibs.co) (community) - **Categories:** MCP servers, Social media, AI - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $5.00 / 1,000 mcp tool calls 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 ## MCP: TikTok Research / Content Intel An **MCP server** that wraps [`tiktok-research-intel`](https://apify.com/seibs.co/tiktok-research-intel) as six AI-agent tools - delivering the **research-equivalent TikTok datasets** the gated TikTok Research API gives academics only, to the commercial users it bars (brands, journalists, agencies, analysts) and to AI agents directly. This server is **x402 (USDC on Base)** and **Skyfire** ready: an AI agent can pay per tool call with no pre-provisioned token when the operator enables MCP monetization. ### Tools | Tool | Args | Returns | |---|---|---| | `hashtag_trend` | `hashtag`, `max_videos`, `region` | Hashtag virality panel: stats, daily time-series, velocity, trend direction, top videos/creators, co-hashtags. | | `keyword_trend` | `keyword`, `max_videos`, `region` | Same panel from TikTok search results. | | `creator_metrics` | `creator`, `max_videos` | Creator research profile: followers, engagement distribution, avg ER, posting cadence, engagement trend. | | `video_engagement` | `video_url` | One video's normalized engagement metrics (views/likes/comments/shares/saves + ER). | | `comment_sentiment` | `video_url` | Comment-sentiment panel: sentiment + theme tags + positivity rate + top emojis. | | `sound_trend` | `hashtag`, `max_videos`, `region` | Per-sound velocity panels mined from the hashtag's videos. | ### Modes - `list_tools` - emit the tool catalog + agentic-payment descriptor (free). - `call_tool` - invoke one tool (`tool` + `args`). - `batch` - run up to 10 `{tool, args}` calls in one actor run. ### Example ```json { "mode": "call_tool", "tool": "hashtag_trend", "args": { "hashtag": "booktok", "max_videos": 50, "region": "US" } } ```` ### Connect it to an MCP client This Actor is exposed as a remote MCP tool through Apify's hosted MCP server. Point any MCP-compatible client (Claude Desktop, Cursor, VS Code, or an OpenAI / LangChain / LlamaIndex agent) at Apify's server with this Actor enabled: ```json { "mcpServers": { "apify": { "url": "https://mcp.apify.com?tools=seibs.co/mcp-tiktok-research-intel", "headers": { "Authorization": "Bearer " } } } } ``` Get a token (free) from **Apify Console -> Settings -> API & Integrations**. The Actor then appears as a callable tool. Run `mode=list_tools` first (free) to fetch every tool's live JSON schema, then call one: ```json { "mode": "call_tool", "tool": "hashtag_trend", "args": { } } ``` **Prefer a direct call?** Hit the Actor straight through the Apify API / SDK - no MCP client required: ```python from apify_client import ApifyClient client = ApifyClient("") run = client.actor("seibs.co/mcp-tiktok-research-intel").call(run_input={ "mode": "call_tool", "tool": "hashtag_trend", "args": {}, }) items = client.dataset(run["defaultDatasetId"]).list_items().items ``` ### Pricing Flat **$0.005 per MCP tool call**, plus the upstream `tiktok-research-intel` PPE pass-through (`video_record` $0.004, `hashtag_trend_enrichment` $0.008, `comment_sentiment` $0.010) billed to the same run. `list_tools` is free. ### Notes The underlying data is **public, logged-out** TikTok content; PII is minimized to public creator handles + public engagement metrics. TikTok's comment API is signed and frequently gates logged-out requests, so `comment_sentiment` can return an empty panel (documented fail-soft). The upstream actor runs on the Playwright image / a warm `BROWSER_CDP_URL` for the gated surfaces. Use responsibly and in line with TikTok's terms and applicable privacy law. # Actor input Schema ## `mode` (type: `string`): list\_tools = emit the MCP tool catalog (free). call\_tool = invoke one tool (requires 'tool' + 'args'). batch = invoke a list of {tool, args} calls. ## `tool` (type: `string`): Required when mode=call\_tool. One of: hashtag\_trend, keyword\_trend, creator\_metrics, video\_engagement, comment\_sentiment, sound\_trend. ## `args` (type: `object`): Arguments for the selected tool. Example for hashtag\_trend: {"hashtag": "booktok", "max\_videos": 50, "region": "US"}. ## `calls` (type: `array`): Required when mode=batch. Each entry is an object with 'tool' and 'args'. Example: \[{"tool": "hashtag\_trend", "args": {"hashtag": "booktok"}}, {"tool": "creator\_metrics", "args": {"creator": "nasa"}}]. Max 10 calls per run. ## Actor input object example ```json { "mode": "list_tools", "tool": "hashtag_trend", "args": { "hashtag": "booktok", "max_videos": 50, "region": "US" }, "calls": [] } ``` # 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 = { "mode": "list_tools", "args": { "hashtag": "booktok", "max_videos": 50, "region": "US" } }; // Run the Actor and wait for it to finish const run = await client.actor("seibs.co/mcp-tiktok-research-intel").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 = { "mode": "list_tools", "args": { "hashtag": "booktok", "max_videos": 50, "region": "US", }, } # Run the Actor and wait for it to finish run = client.actor("seibs.co/mcp-tiktok-research-intel").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 '{ "mode": "list_tools", "args": { "hashtag": "booktok", "max_videos": 50, "region": "US" } }' | apify call seibs.co/mcp-tiktok-research-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seibs.co/mcp-tiktok-research-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MCP: TikTok Research / Content Intel - Trends & Creators", "description": "MCP server for tiktok-research-intel. AI-agent tools: hashtag_trend, keyword_trend, creator_metrics, video_engagement, comment_sentiment, and sound_trend - the research-grade TikTok datasets the gated Research API gives academics only. x402 and Skyfire ready. For brands and analysts.", "version": "0.1", "x-build-id": "Qqu7R1ZlvZHrhGaGR" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seibs.co~mcp-tiktok-research-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seibs.co-mcp-tiktok-research-intel", "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/seibs.co~mcp-tiktok-research-intel/runs": { "post": { "operationId": "runs-sync-seibs.co-mcp-tiktok-research-intel", "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/seibs.co~mcp-tiktok-research-intel/run-sync": { "post": { "operationId": "run-sync-seibs.co-mcp-tiktok-research-intel", "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": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "list_tools", "call_tool", "batch" ], "type": "string", "description": "list_tools = emit the MCP tool catalog (free). call_tool = invoke one tool (requires 'tool' + 'args'). batch = invoke a list of {tool, args} calls.", "default": "list_tools" }, "tool": { "title": "Tool name", "enum": [ "hashtag_trend", "keyword_trend", "creator_metrics", "video_engagement", "comment_sentiment", "sound_trend" ], "type": "string", "description": "Required when mode=call_tool. One of: hashtag_trend, keyword_trend, creator_metrics, video_engagement, comment_sentiment, sound_trend.", "default": "hashtag_trend" }, "args": { "title": "Tool arguments (JSON object)", "type": "object", "description": "Arguments for the selected tool. Example for hashtag_trend: {\"hashtag\": \"booktok\", \"max_videos\": 50, \"region\": \"US\"}." }, "calls": { "title": "Batch calls", "maxItems": 10, "type": "array", "description": "Required when mode=batch. Each entry is an object with 'tool' and 'args'. Example: [{\"tool\": \"hashtag_trend\", \"args\": {\"hashtag\": \"booktok\"}}, {\"tool\": \"creator_metrics\", \"args\": {\"creator\": \"nasa\"}}]. Max 10 calls per run.", "default": [] } } }, "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 } } } } } } } } } } ```