模型上下文协议服务器通过 Ticketmaster Discovery API 提供发现事件、场地和景点的工具。
通过灵活的过滤功能搜索活动、场地和景点：
关键词搜索
事件的日期范围
位置（城市、州、国家）
特定场地搜索
特定景点搜索
事件分类/类别
输出格式：
用于编程的结构化 JSON 数据
可供直接使用的人类可读文本
综合数据包括：
姓名和身份证
日期和时间（活动）
价格范围（针对活动）
网址
图片
地点和地址（场馆）
景点分类
Related MCP server: Ticketmaster Partner API
npx -y install @delorenj/mcp-server-ticketmaster服务器需要 Ticketmaster API 密钥。您可以通过以下方式获取：
创建帐户或登录
前往您帐户中的“我的应用”
创建新应用以获取 API 密钥
在您的 MCP 设置文件中设置您的 API 密钥：
{
"mcpServers": {
"ticketmaster": {
"command": "npx",
"args": ["-y", "@delorenj/mcp-server-ticketmaster"],
"env": {
"TICKETMASTER_API_KEY": "your-api-key-here"
}
}
}
}服务器提供了一个名为search_ticketmaster的工具，它接受：
type ：搜索类型（“活动”、“场地”或“景点”）
keyword ：搜索词
startDate ：YYYY-MM-DD 格式的开始日期（用于事件）
endDate ：YYYY-MM-DD 格式的结束日期（对于事件）
city ：城市名称
stateCode ：州代码（例如“NY”）
countryCode ：国家代码（例如“US”）
venueId ：具体场地 ID
attractionId ：具体景点ID
classificationName ：事件类别（例如，“体育”、“音乐”）
format ：输出格式（“json”或“text”，默认为“json”）
<use_mcp_tool>
<server_name>ticketmaster</server_name>
<tool_name>search_ticketmaster</tool_name>
<arguments>
{
"type": "event",
"keyword": "concert",
"startDate": "2025-02-01",
"endDate": "2025-02-28",
"city": "New York",
"stateCode": "NY"
}
</arguments>
</use_mcp_tool><use_mcp_tool>
<server_name>ticketmaster</server_name>
<tool_name>search_ticketmaster</tool_name>
<arguments>
{
"type": "event",
"keyword": "concert",
"startDate": "2025-02-01",
"endDate": "2025-02-28",
"city": "New York",
"stateCode": "NY",
"format": "text"
}
</arguments>
</use_mcp_tool>克隆存储库
复制示例环境文件：
cp .env.example .env将您的 Ticketmaster API 密钥添加到.env
安装依赖项：
npm install构建项目：
npm run build与检查员一起测试：
npm run inspector欢迎贡献代码！欢迎提交 Pull 请求。对于重大变更，请先提交一个 issue 来讨论您想要修改的内容。
MIT 许可证 - 详情请参阅许可证文件
Search for events, venues, or attractions on Ticketmaster
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Type of search to perform | |
| keyword | No | Search keyword or term | |
| startDate | No | Start date in YYYY-MM-DD format | |
| endDate | No | End date in YYYY-MM-DD format | |
| city | No | City name | |
| stateCode | No | State code (e.g., NY, CA) | |
| countryCode | No | Country code (e.g., US, CA) | |
| venueId | No | Specific venue ID to search | |
| attractionId | No | Specific attraction ID to search | |
| classificationName | No | Event classification/category (e.g., "Sports", "Music") | |
| format | No | Output format (defaults to json) | json |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but provides minimal behavioral insight. It mentions searching but doesn't disclose rate limits, authentication needs, pagination, error handling, or what happens with partial/no results. This is inadequate for a search tool with 11 parameters.
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 a single, efficient sentence that front-loads the core purpose without unnecessary words. It earns its place by clearly stating the tool's function in minimal space.
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?
For a complex search tool with 11 parameters, no annotations, and no output schema, the description is insufficient. It lacks details on behavior, output format (beyond the 'format' parameter), error cases, and usage context, leaving significant gaps for an AI agent.
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%, so the schema fully documents all parameters. The description adds no additional meaning beyond implying search across multiple resource types, which aligns with the 'type' parameter. Baseline 3 is appropriate as the schema does the heavy lifting.
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 verb ('Search') and resources ('events, venues, or attractions on Ticketmaster'), making the purpose immediately understandable. It doesn't need to distinguish from siblings since none exist, but it could be more specific about what 'search' entails (e.g., listing vs. filtering).
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?
No guidance is provided on when to use this tool versus alternatives or any prerequisites. The description merely states what it does without context about appropriate scenarios, limitations, or integration with other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.2.5search_ticketmasterScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as searching for events, venues, or attractions, making it straightforward for an agent to understand its function without confusion.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'search_ticketmaster' follows a clear verb_noun pattern, which would be consistent if more tools were added, but as a standalone, it sets a good precedent.
A single tool is too few for a server focused on Ticketmaster events, which typically involves operations like browsing events, getting details, checking availability, or purchasing tickets. This minimal set feels thin and incomplete for the apparent scope, limiting agent capabilities.
The tool surface is severely incomplete for the domain of Ticketmaster events. While search is a useful starting point, there are obvious gaps such as retrieving event details, listing venues, checking ticket availability, or handling bookings, which are essential for comprehensive event management workflows.
Ticketmaster Discovery v2 MCP.
Google Events listings with dates, venues, and ticket links via a hosted MCP server.
Live event ticket market data: prices, inventory, demand and seat maps, with screens and alerts.
European and US event data from Eventbrite: dates, venues, categories, ticket prices, organizers.