A production-ready MCP (Model Context Protocol) OAuth 2.1 server implementation built with Next.js 15, providing secure authentication and analytics
A production-ready MCP (Model Context Protocol) OAuth 2.1 server implementation built with Next.js 15, providing secure authentication and analytics for MCP clients. That is what the mcp oauth sample mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
The server publishes 2 tools. What each one is for:
Dashboard — The Dashboard tool exposed by this serverCommunity — The Community tool exposed by this serverSetup 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. MCP Oauth Sample's toolset — Dashboard, Community — is a fair guide to whether it matches your workflow. It is maintained by raxITai; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against MCP Oauth Sample's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| Dashboard | The Dashboard tool exposed by this server. |
| Community | The Community tool exposed by this server. |
### For Cursor
```json
{
"mcpServers": {
"raxIT-oauth": {
"url": "https://your-domain.com/mcp/mcp",
"transport": "http-stream"
}
}
}Configuration as documented by the project. Restart the client after saving.
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.