"Ray" matching MCP servers:

Enables governance-grade AIops for GPU inference clusters with root-cause analysis, metrics, and policy-governed operations for vLLM and Ray.

MCP server for analyzing OpenTelemetry traces with performance and error diagnosis, supporting loading from files, AWS X-Ray, and CloudWatch.

MCP Server for Raygun's API V3 endpoints for interacting with your Crash Reporting and Real User Monitoring applications. This server provides comprehensive access to Raygun's API features through the Model Context Protocol.

Enables MCP clients to interact with a fictional bank through remote tools, including mock banking operations, OAuth 2.1 authentication, and a live X-ray dashboard for observing session activity.

Enables AI assistants to monitor and troubleshoot AWS Application Signals services by tracking service health, analyzing SLO compliance, querying CloudWatch metrics, and investigating issues using distributed tracing with AWS X-Ray.

Enables read-only interaction with official Embrace MCP tools while preserving full structured response data, including session aggregates and detailed logs, crashes, network, and spans, through a local stdio server.

A Model Context Protocol (MCP) server that provides comprehensive access to IBM QRadar security intelligence platform. Query logs, events, offenses, agents, assets, and more directly from your AI assistant.

Exposes knowledge retrieval to AI tools over MCP with dense, sparse, and hybrid search, RRF fusion, configurable reranking, and modular ingestion of PDF/Markdown documents including image captioning. It also provides query tracing and Ragas-based evaluation so pipelines can be swapped, debugged, and tested independently.

Enables plant operators to query alarm data, asset information, trends, correlations, and operator recommendations through 14 tools, forming a core part of a multi-MCP enterprise operations copilot.

A modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.

Enables cloud and platform engineers to investigate AWS incidents by grounding responses in approved runbooks and operational evidence, with authenticated access, citations, observability, and human approval before any write action.