MCP server for AppSignal error monitoring integration
Appsignal MCP MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. MCP server for AppSignal error monitoring integration.
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.
You will need one environment variable: APPSIGNAL_API_TOKEN. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Among the monitoring and observability options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. It is maintained by pauldub; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
{
"mcpServers": {
"appsignal-mcp": {
"type": "stdio",
"command": "bun",
"args": [
"run",
"start"
],
"env": {
"APPSIGNAL_API_TOKEN": "your-api-token"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| APPSIGNAL_API_TOKEN | Credential the server authenticates with. | Yes |
Give your coding agent the full DevTools toolbox: traces, network, console, heap snapshots and Lighthouse.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.
Errors with full context — stack traces, issue triage and AI-powered root-cause analysis from Sentry's server.
Enables enhanced web research capabilities for large language models through intelligent search queuing and advanced content extraction.
Automates browser interactions and enables Large Language Models (LLMs) to interact with web pages through Playwright and Chrome DevTools Protocol
Guides tool usage by providing recommendations for MCP tools at each problem-solving stage.