tickadoo
OfficialServidor MCP de tickadoo®
El servidor MCP de tickadoo® acerca el descubrimiento de experiencias en vivo a los asistentes de IA a través del Protocolo de Contexto de Modelos (MCP). Ofrece a los clientes compatibles acceso para reservar teatro, espectáculos, tours, atracciones y eventos en más de 680 ciudades de todo el mundo.
Nota interna: Este es el MCP público orientado a agentes en
mcp.tickadoo.com, la interfaz que soporta Claude, ChatGPT, Cursor, Smithery y otros clientes MCP. Un endpoint interno separado/mcp-dbque leía la base de datos de producción para investigaciones ad-hoc del equipo fue retirado tras el cambio; ha sido reemplazado por la habilidaddb-investigatede Claude en el repositorio howard. Cuando los documentos del equipo de tickadoo dicen "MCP" sin calificativo, se refieren a este servidor.
🌐 Idiomas: Página de inicio disponible en: EN · JA · 中文 · 한국어 · DE · FR · ES · IT · PT · tlhIngan Hol 🖖
日本語
tickadoo® MCPサーバーは、AIアシスタント(Claude、ChatGPT、Gemini等)から680以上の都市で13,000以上の体験(劇場、ツアー、アトラクション)を検索できます。APIキー不要、認証不要、即座に接続可能です。
中文
tickadoo® MCP服务器让AI助手(Claude、ChatGPT、Gemini等)可以搜索680+城市中13,000+体验活动(剧院、旅游、景点)。无需API密钥,无需认证,即时连接。
한국어
tickadoo® MCP 서버는 AI 어시스턴트(Claude, ChatGPT, Gemini 등)에서 680+ 도시의 13,000+ 체험(극장, 투어, 관광명소)을 검색할 수 있습니다.
Versión actual: v1.4.2
17 herramientas MCP de solo lectura con flujos de trabajo de búsqueda, comparación, guía de ciudad, día en familia, traslados y consejos de viaje locales
13,090 productos en 681 ciudades en más de 40 idiomas
Enriquecimiento de audiencia + etiquetas basado en reglas (cobertura del 100%)
Espacios de disponibilidad con fechas y precios reales
Contrato de reserva con enlaces directos de pago + Ghost Checkout
Datos de accesibilidad para 30 teatros de Londres
Capa completa de inteligencia de agentes en ambas herramientas de búsqueda:
_available_filters(10 campos: tag_counts, price_range, duration_range, audience, setting, physical, languages, tags, wheelchair, free_cancellation)_conversation_starters(prompts contextuales basados en datos de resultados)_related_searches(sugerencias de seguimiento basadas en etiquetas)_booking_urgency(señales de conversión: disponibilidad HOY, cancelación gratuita, calificación, silla de ruedas)_cross_sell,_intent_token,_accessibility📊 Línea de resumen de resultados + 🔍 sugerencias de filtros en respuestas de texto
🔥 Señales de urgencia en respuestas detalladas de texto
Recuperación inteligente de filtros (guía a los agentes cuando los filtros son demasiado restrictivos)
1 recurso de feed de productos legible por máquina
Endpoint HTTP remoto para clientes MCP alojados
Punto de entrada stdio local para flujos de trabajo de escritorio y desarrollo
Herramientas
Herramienta | Descripción |
| Busca más de 13,090 experiencias en 681 ciudades con 11 filtros (audiencia, entorno, silla de ruedas, nivel físico, duración, idioma, calificación, cancelación, precio, etiquetas, categoría) + 6 opciones de ordenación (incl. best_value) |
| Busca por intención emocional (romántico, relajante, económico, día lluvioso, aventurero, gastronómico) — mapea el estado de ánimo a filtros de etiquetas, audiencia, calificación, entorno y precio |
| Encuentra experiencias cerca de lat/lng con los mismos 11 filtros, radio configurable y filtrado por fecha |
| Explora ciudades compatibles con filtrado opcional y límites de resultados |
| Verificación rápida de disponibilidad por fecha para una experiencia específica — devuelve espacios coincidentes, precio por persona, el total más barato para |
| Disponibilidad detallada, precios, lugar e información de imagen para una experiencia específica de tickadoo por slug o ruta de reserva |
| Comparación lado a lado de 2-5 experiencias con destacados para mejor valor, mejor calificado, más popular y apto para familias |
| Planificador de ciudad de 7 días con desglose de mañana/tarde/noche |
| Experiencias de esta noche con clasificación por hora de inicio y señales de urgencia |
| Experiencias que comienzan en pocas horas, ordenadas por las más próximas |
| Resumen de ciudad curado: puntos destacados, mezcla de categorías, rangos de precios, sugerencias de mejores opciones, consejos de expertos |
| Consejos locales para 20 ciudades de lanzamiento: transporte, dinero, seguridad, cultura, comida, números de emergencia, frases rápidas |
| Estimaciones de traslados en taxi, metro, autobús y tren desde el aeropuerto, estación o puerto predeterminado de una ciudad hasta las coordenadas del hotel |
| Planificador de día en familia con filtrado según la edad y agrupación geográfica |
| Resultados combinados semánticos + heurísticos de "también te podría gustar" para una experiencia dada |
Todas las herramientas exponen anotaciones de herramientas MCP para readOnlyHint, destructiveHint y openWorldHint.
Capa de Inteligencia de Agentes
Cada respuesta de búsqueda incluye claves de metadatos diseñadas para agentes de IA:
Clave | Descripción |
| 12 campos: etiquetas, audiencia, entorno, rango_de_precio, rango_de_duración, etc. |
| Top 3 auto-curado: mejor_valor, mejor_calificado, más_popular |
| Agrupación de presupuesto/rango medio/premium a partir de los precios de los resultados |
| Desglose de categorías basado en etiquetas (ej. "8 Cruceros, 6 Tours guiados, 5 Cenas") |
| Prompts conscientes del contexto que hacen referencia a productos reales |
| Sugerencias basadas en etiquetas para reducir resultados |
| Acción de seguimiento recomendada |
get_experience_details añade: _booking_urgency, _cross_sell, _intent_token, _accessibility
Related MCP server: Nightlife Search
Recurso
Recurso | Descripción |
| Feed de productos legible por máquina en formato OpenAI Commerce Product Feed |
Conectar
Endpoint MCP alojado:
https://mcp.tickadoo.com/mcp
Claude / Cursor
Ejemplo de configuración de MCP remoto:
{
"mcpServers": {
"tickadoo": {
"url": "https://mcp.tickadoo.com/mcp"
}
}
}Claude Code
MCP HTTP remoto (recomendado — sin clave API, sin instalación local):
claude mcp add --transport http tickadoo https://mcp.tickadoo.com/mcpO añadir como stdio local desde npm:
claude mcp add tickadoo -- npx -y @tickadoo/mcp-serverConsulta https://code.claude.com/docs/en/mcp para ver la documentación completa de Claude Code MCP.
Claude Dispatch
Claude Dispatch utiliza la misma configuración de URL de MCP remoto:
{
"mcpServers": {
"tickadoo": {
"url": "https://mcp.tickadoo.com/mcp"
}
}
}Gemini CLI
Añade esto a ~/.gemini/settings.json:
{
"mcpServers": {
"tickadoo": {
"httpUrl": "https://mcp.tickadoo.com/mcp"
}
}
}Perplexity
Utiliza el conector remoto personalizado de Perplexity:
Ajustes → Conectores → Conector personalizado → Remoto
Nombre:
tickadooURL del servidor MCP:
https://mcp.tickadoo.com/mcpTransporte:
Streamable HTTPAutenticación:
NingunaDisponible para suscriptores de pago de Perplexity
ChatGPT
Disponible para planes ChatGPT Pro, Business, Enterprise y Edu:
Abre Ajustes → Apps y conectores → Avanzado → Modo desarrollador ACTIVADO
De vuelta en Apps y conectores → Crear
Nombre:
tickadooURL:
https://mcp.tickadoo.com/mcpAutenticación:
NingunaEn un nuevo chat: + → Más → Modo desarrollador → Añadir fuentes → tickadoo
Android Studio / Firebase Studio
La misma configuración httpUrl funciona en:
Android Studio (
mcp.json)Firebase Studio (
.idx/mcp.jsono.gemini/settings.json)
Desarrollo Local y Stdio
Ejecutar desde npm después de publicar:
npx -y @tickadoo/mcp-serverEjemplo de configuración stdio vía npx:
{
"mcpServers": {
"tickadoo": {
"command": "npx",
"args": ["-y", "@tickadoo/mcp-server"]
}
}
}Instalar y compilar:
npm install
npm run buildEjecutar el servidor stdio directamente:
node dist/index.jsEjemplo de configuración stdio:
{
"mcpServers": {
"tickadoo": {
"command": "node",
"args": ["/path/to/tickadoo-mcp/dist/index.js"]
}
}
}Enlaces Publicados
Página de inicio: https://mcp.tickadoo.com
Endpoint MCP remoto: https://mcp.tickadoo.com/mcp
tickadoo: https://www.tickadoo.com
Comandos de Desarrollo
Comando | Propósito |
| Empaqueta el servidor stdio ( |
| Ejecuta la suite de pruebas unitarias (vitest) |
| Ejecuta la suite de pruebas de humo MCP contra el servidor stdio local |
| Ejecuta la suite de pruebas de humo MCP contra un endpoint HTTP |
| Ejecuta el Worker de producción localmente vía |
| Despliega el Worker MCP principal ( |
| Despliega el Worker de widgets bajo |
Arquitectura
La lógica del servidor compartido reside en src/shared/* — cliente API, formato, definiciones de herramientas, registro de recursos. Wrappers de transporte:
src/index.ts— transporte stdio local (publicado en npm como@tickadoo/mcp-server)src/worker.ts— Cloudflare Worker (Hono +WebStandardStreamableHTTPServerTransport) sirviendohttps://mcp.tickadoo.comwidgets-worker/— Cloudflare Worker separado sirviendo widgets incrustables enhttps://widgets.tickadoo.com
Despliegue
Ambos workers se despliegan vía Cloudflare Workers:
Worker MCP principal:
wrangler.jsonc→mcp.tickadoo.comWorker de widgets:
widgets-worker/wrangler.jsonc→widgets.tickadoo.com
CI: .github/workflows/deploy-cf.yml se ejecuta en cada push a main y despliega ambos workers.
Variables de Entorno
Variable | Predeterminado | Descripción |
|
| URL base de la API de tickadoo |
|
| Verbosidad de registro: |
|
| URL base del sitio web de tickadoo |
Marca
tickadoo® siempre va en minúsculas. El eslogan es What Do You Wanna Doo?® Por favor, mantén las minúsculas en todas las integraciones y documentación.
Licencia
MIT — tickadoo Inc.
Available Tools
4 toolsfind_nearby_experiencesARead-onlyInspect
Find shows, events and experiences near a geographic location on tickadoo®. Use when a user shares their location or asks for things to do near them.
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | Latitude | |
| longitude | Yes | Longitude | |
| radius_km | No | Search radius in km (default 25) | |
| language | No | Language code | en |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the agent knows this is a safe, non-destructive query operation. The description adds context about location-based searching but doesn't disclose additional behavioral traits like rate limits, authentication needs, or result format. With annotations covering the core safety profile, a 3 is appropriate as the description provides some useful context without rich behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and followed by usage guidance. Every sentence earns its place by providing essential information without redundancy or fluff, making it highly efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (location-based search), rich annotations (read-only, open-world), and full schema coverage, the description is mostly complete. It lacks details on output format (no output schema provided) and doesn't mention pagination or result limits, but the purpose and usage are well-covered, making it adequate for agent selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all parameters (latitude, longitude, radius_km, language) documented in the schema. The description doesn't add meaning beyond the schema, such as explaining coordinate systems or language code specifics. Baseline 3 is correct when the schema fully describes parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Find shows, events and experiences near a geographic location on tickadoo®.' It specifies the verb ('Find'), resource ('shows, events and experiences'), and context ('near a geographic location'), distinguishing it from sibling tools like get_experience_details (detail view) and list_cities (city listing).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Use when a user shares their location or asks for things to do near them.' This clearly indicates when to invoke this tool versus alternatives, helping the agent select it based on user context rather than other tools like search_experiences (which might not be location-based).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_experience_detailsARead-onlyInspect
Get detailed availability, venue details, and images for a specific tickadoo® experience. Prefer passing the tickadoo slug or booking URL path; provider and provider_id are legacy fallback inputs.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | No | Preferred: tickadoo slug or path, e.g. 'london-dungeon-tickets' or '/london/london-dungeon-tickets' | |
| provider | No | Legacy fallback only: hidden provider name used internally | |
| provider_id | No | Legacy fallback only: hidden provider-specific product ID | |
| days | No | Number of days of availability to fetch (default 30, max 180) | |
| language | No | Reserved for future language-aware API support | en |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only, non-destructive, and open-world behavior, which the description does not contradict. It adds context by specifying input preferences and the scope of data fetched (availability, venue details, images), enhancing understanding beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and usage guidelines in two concise sentences, with no wasted words. Every sentence contributes directly to tool understanding and selection.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, no output schema) and rich annotations, the description is mostly complete. It covers purpose, usage, and input semantics well, but could benefit from mentioning potential outputs or limitations, though annotations help mitigate this gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema fully documents all parameters. The description adds value by clarifying input preferences (slug as preferred, provider/provider_id as legacy fallbacks) but does not provide additional semantic details beyond what the schema already covers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get detailed availability, venue details, and images') and the resource ('a specific tickadoo® experience'), distinguishing it from sibling tools like find_nearby_experiences, list_cities, and search_experiences by focusing on details for a single experience rather than searching or listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit guidance on when to use this tool by specifying preferred inputs ('tickadoo slug or booking URL path') and fallback options ('provider and provider_id are legacy fallback inputs'), helping the agent choose this over alternatives for retrieving detailed information on a known experience.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_citiesARead-onlyInspect
List all cities where tickadoo® has bookable experiences. Use to help users discover available destinations.
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | Language code | en |
| query | No | Optional city name or slug filter (e.g. 'new', 'paris', 'tokyo') | |
| limit | No | Maximum number of cities to return (default 50) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, covering safety and scope. The description adds minimal behavioral context beyond this, such as the purpose of 'discover available destinations,' but doesn't detail aspects like rate limits, authentication needs, or pagination behavior. It doesn't contradict annotations, so it meets the baseline for having annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded: two sentences that directly state the tool's purpose and usage without unnecessary details. Every sentence earns its place by providing essential information, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (a read-only list operation), rich annotations (covering safety and scope), and 100% schema coverage, the description is mostly complete. It lacks an output schema, but the description doesn't need to explain return values. However, it could be more complete by mentioning potential limitations or result formats, slightly reducing the score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain the 'query' parameter's filtering behavior in more detail). With high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't need to.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List all cities where tickadoo® has bookable experiences.' It specifies the verb ('List') and resource ('cities'), and distinguishes it from siblings by focusing on destinations rather than experiences. However, it doesn't explicitly differentiate from potential sibling tools like 'search_cities' if they existed, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage context: 'Use to help users discover available destinations.' This suggests it's for discovery purposes, but it doesn't explicitly state when to use this tool versus alternatives like 'search_experiences' or 'find_nearby_experiences' from the sibling list. No exclusions or clear alternatives are mentioned, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_experiencesARead-onlyInspect
Search for shows, theatre, events, tours and experiences in a specific city on tickadoo®. Use when a user asks what to do in a city, wants event/show recommendations, or is looking for tickets.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | City name or slug (e.g. 'london', 'new-york', 'paris', 'tokyo', 'dubai') | |
| language | No | Language code (e.g. 'en', 'de', 'fr', 'es') | en |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the agent knows this is a safe, read-only search operation with open-world semantics. The description adds useful context about the types of content searched (shows, theatre, events, tours, experiences) and the tickadoo® platform, but doesn't provide additional behavioral details like rate limits, authentication needs, or pagination behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise and front-loaded: the first sentence states the core functionality, and the second sentence provides clear usage guidelines. Every sentence earns its place with no wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this is a search tool with good annotations (read-only, open-world) and 100% schema coverage, the description provides adequate context about what's being searched and when to use it. However, without an output schema, the description doesn't explain what the search returns (e.g., result format, pagination), leaving a minor gap in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters ('city' and 'language') fully documented in the schema. The description doesn't add any parameter-specific semantics beyond what's already in the schema (e.g., it doesn't explain city validation rules or language code formats). With complete schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search for shows, theatre, events, tours and experiences') and resource ('in a specific city on tickadoo®'), distinguishing it from siblings like 'find_nearby_experiences' (which implies proximity-based search) and 'get_experience_details' (which retrieves specific item details). The verb 'search' is precise and the resource scope is well-defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides when-to-use guidance: 'Use when a user asks what to do in a city, wants event/show recommendations, or is looking for tickets.' This gives clear context for invocation and distinguishes it from alternatives like 'list_cities' (which lists available cities rather than searching within one).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
4 tool updates
v1.1.0- First observed
find_nearby_experiences - First observed
get_experience_details - First observed
list_cities - First observed
search_experiences
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: find_nearby_experiences uses geographic location, get_experience_details retrieves details for a specific experience, list_cities enumerates available destinations, and search_experiences searches within a specific city. The descriptions explicitly differentiate their use cases, eliminating any ambiguity.
All tool names follow a consistent verb_noun pattern (e.g., find_nearby_experiences, get_experience_details, list_cities, search_experiences) with clear, descriptive verbs and nouns. There are no deviations in style or convention, making the set highly predictable and readable.
With 4 tools, the set is well-scoped for a ticketing/experience discovery server, covering key functionalities like location-based finding, detailed lookups, city listing, and city-specific searches. Each tool earns its place without redundancy or excessive complexity.
The tool surface covers core discovery and information retrieval workflows effectively, including location-based and city-specific searches, detailed views, and destination listing. A minor gap exists in booking or transaction capabilities, but agents can work around this for recommendation and exploration tasks.
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