AppSignal MCP

AppSignal MCP

Enables AI assistants to query and fetch error and performance monitoring data from AppSignal through the Model Context Protocol. Supports searching and retrieving detailed information about application errors and performance samples with flexible filtering options.

Category
访问服务器

README

AppSignal MCP

A Model Context Protocol (MCP) server for AppSignal monitoring API integration. This server allows AI assistants to directly query and fetch error and performance data from AppSignal through the MCP protocol.

Features

  • Fetch details about specific error or performance samples
  • Search for errors, performance samples, or both with flexible filters
  • Integration with AppSignal's Error and Performance Monitoring APIs

Prerequisites

  • Bun runtime
  • AppSignal account and API token
  • Application ID from your AppSignal dashboard

Installation

# Clone the repository
git clone https://github.com/pauldub/appsignal-mcp.git
cd appsignal-mcp

# Install dependencies
bun install

Configuration

Create a .env file in the root directory with your AppSignal credentials:

# Server configuration
PORT=3000
LOG_LEVEL=info

# AppSignal configuration
APPSIGNAL_API_TOKEN=your-api-token

Usage

Starting the Server

# Run the server
bun start

# Development mode with auto-reload
bun dev

# Run tests
bun test

# Build a standalone executable
bun run build

CLI Options

appsignal-mcp --appsignal-api-token your-token

Available options:

  • --appsignal-api-token <token> - AppSignal API token
  • --log-level <level> - Logging level (debug, info, warn, error)
  • --port <port> - Server port number

MCP Tools

1. get_sample

Gets details about a specific sample by ID (error or performance).

Parameters:

  • sampleId (string, required): The AppSignal sample ID
  • appId (string, required): The AppSignal application ID

2. search_samples

Searches for samples in an application with optional filters.

Parameters:

  • appId (string, required): The AppSignal application ID
  • sample_type (string, optional): Type of samples to search - "all", "errors", or "performance" (defaults to "errors")
  • exception (string, optional): Filter by exception name (e.g., "NoMethodError") - only applicable for error samples
  • action_id (string, optional): Filter by action name (e.g., "BlogPostsController-hash-show")
  • since (string/number, optional): Start timestamp in UTC (timestamp or ISO format)
  • before (string/number, optional): End timestamp in UTC (timestamp or ISO format)
  • limit (number, optional): Maximum number of samples to return (defaults to 10)
  • count_only (boolean, optional): Only return the count, not the samples

Claude Integration

To use this MCP server with Claude, create a .mcp.json file in your Claude Code workspace:

{
  "mcpServers": {
    "appsignal-mcp": {
      "type": "stdio",
      "command": "bun",
      "args": [
        "run",
        "start"
      ],
      "env": {
        "APPSIGNAL_API_TOKEN": "your-api-token"
      }
    }
  }
}

License

MIT

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选