# MCP: Eventbrite Events Intel - Discovery, Prices, Organizers (`seibs.co/mcp-eventbrite-events-intel`) Actor MCP server for eventbrite-events-intel. AI-agent tools: discover\_events, get\_event\_details (price tiers, organizer, performers), and get\_organizer\_events - the event-discovery layer Eventbrite removed from its API in 2020. x402 (USDC on Base) and Skyfire ready. For event marketers and sponsors. - **URL**: https://apify.com/seibs.co/mcp-eventbrite-events-intel.md - **Developed by:** [Seibs.co](https://apify.com/seibs.co) (community) - **Categories:** MCP servers, Business, Developer tools - **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: Eventbrite Events Intel **The Model Context Protocol server for [eventbrite-events-intel](https://apify.com/seibs.co/eventbrite-events-intel) - give your AI agent the Eventbrite event-discovery layer the API can't.** Eventbrite removed its public Event Search API in 2020; cross-organizer event discovery is impossible via the official API. This MCP server exposes that discovery as clean, agent-ready tools so a Claude / OpenAI / LangChain agent can find, enrich, and track public Eventbrite events directly. ### Tools | Tool | What it does | |---|---| | `discover_events` | Find public events by location, category, keyword, and date; returns normalized events + per-organizer rollups. Optional `include_pricing` adds price tiers + performers. | | `get_event_details` | Enrich specific event URLs with price range / ticket tiers, availability, organizer, performers, and venue. | | `get_organizer_events` | Pull an organizer's public upcoming events from their `/o/` page, plus a rollup of their event profile. | ### Modes - `list_tools` (default, free) - emit the tool catalog + the agentic-payment descriptor. - `call_tool` - invoke one tool: `{ "mode": "call_tool", "tool": "discover_events", "args": { "location": "ny--new-york", "category": "music", "keyword": "jazz" } }` - `batch` - run several `{tool, args}` calls in one run (max 10). ### Example ```json { "mode": "call_tool", "tool": "discover_events", "args": { "location": "ca--san-francisco", "category": "business", "date_from": "2026-07-01", "date_to": "2026-07-31", "include_pricing": true, "limit": 25 } } ```` Every tool returns `{ tool, args, ok, count, items, summary, error }`. Tools never throw - upstream failure becomes `ok: false` with an `error` string. ### 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-eventbrite-events-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": "discover_events", "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-eventbrite-events-intel").call(run_input={ "mode": "call_tool", "tool": "discover_events", "args": {}, }) items = client.dataset(run["defaultDatasetId"]).list_items().items ``` ### Pricing Flat **$0.005 per MCP tool call**, plus the upstream `eventbrite-events-intel` PPE events (event\_record $0.005, price\_ticket\_enrichment $0.010, organizer\_rollup $0.015) billed to the same run as pass-through. ### Agentic payments (x402 + Skyfire) This server is **x402 (USDC on Base)** and **Skyfire** ready - an AI agent can pay per call with no pre-provisioned API token when the operator enables Apify MCP monetization. The `list_tools` catalog advertises the accepted rails. Configure via env: `X402_ENABLED`, `X402_PAY_TO_ADDRESS`, `X402_PRICE_USD`, `SKYFIRE_ENABLED`, `SKYFIRE_SELLER_ID`. ### Responsible use Logged-out public Eventbrite event/organizer/venue data only. No login, no attendee/buyer PII, polite rate limits, fail-soft when blocked. See the upstream actor for the full legal posture. # 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: discover\_events, get\_event\_details, get\_organizer\_events. ## `args` (type: `object`): Arguments for the selected tool. Example for discover\_events: {"location": "ny--new-york", "category": "music", "keyword": "jazz"}. ## `calls` (type: `array`): Required when mode=batch. Each entry is an object with 'tool' and 'args'. Example: \[{"tool": "discover\_events", "args": {"location": "ny--new-york", "category": "music"}}]. Max 10 calls per run. ## Actor input object example ```json { "mode": "list_tools", "tool": "discover_events", "args": { "location": "ny--new-york", "category": "music" }, "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": { "location": "ny--new-york", "category": "music" } }; // Run the Actor and wait for it to finish const run = await client.actor("seibs.co/mcp-eventbrite-events-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": { "location": "ny--new-york", "category": "music", }, } # Run the Actor and wait for it to finish run = client.actor("seibs.co/mcp-eventbrite-events-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": { "location": "ny--new-york", "category": "music" } }' | apify call seibs.co/mcp-eventbrite-events-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seibs.co/mcp-eventbrite-events-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MCP: Eventbrite Events Intel - Discovery, Prices, Organizers", "description": "MCP server for eventbrite-events-intel. AI-agent tools: discover_events, get_event_details (price tiers, organizer, performers), and get_organizer_events - the event-discovery layer Eventbrite removed from its API in 2020. x402 (USDC on Base) and Skyfire ready. For event marketers and sponsors.", "version": "0.1", "x-build-id": "rscwbWiFEA3YMkzTw" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seibs.co~mcp-eventbrite-events-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seibs.co-mcp-eventbrite-events-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-eventbrite-events-intel/runs": { "post": { "operationId": "runs-sync-seibs.co-mcp-eventbrite-events-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-eventbrite-events-intel/run-sync": { "post": { "operationId": "run-sync-seibs.co-mcp-eventbrite-events-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": [ "discover_events", "get_event_details", "get_organizer_events" ], "type": "string", "description": "Required when mode=call_tool. One of: discover_events, get_event_details, get_organizer_events.", "default": "discover_events" }, "args": { "title": "Tool arguments (JSON object)", "type": "object", "description": "Arguments for the selected tool. Example for discover_events: {\"location\": \"ny--new-york\", \"category\": \"music\", \"keyword\": \"jazz\"}." }, "calls": { "title": "Batch calls", "maxItems": 10, "type": "array", "description": "Required when mode=batch. Each entry is an object with 'tool' and 'args'. Example: [{\"tool\": \"discover_events\", \"args\": {\"location\": \"ny--new-york\", \"category\": \"music\"}}]. 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 } } } } } } } } } } ```