"Agentic RAG: Understanding or Exploring Its Meaning and Applications" matching MCP servers:

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

Enables alarm investigation through MCP tools for asset search, metadata lookup, alarm retrieval, summarization, priority scoring, and operator recommendations, with RAG-backed evidence from procedure documents.

Enables ingestion and full-text search of Windows application logs via MCP tools. Supports dual-backend indexing with Elasticsearch and SQLite for fast retrieval.

AI-powered MCP server for enterprise OpenShift/Kubernetes cluster management, providing diagnostic tools, RAG knowledge retrieval, and autonomous remediation recommendations.

Provides unified AI agent observability including tracing, cost tracking, performance monitoring, anomaly detection, and audit trails via MCP.

Enables named, authenticated callers to run read-only introspection of a Google Cloud project and to read and write per-tenant notes that stay isolated between callers. Every request is scope-checked against a reviewed policy, rate-limited per caller, and audited without ever logging argument values.

Unified MCP server for DeepAgentLabs that exposes workflow profiling, analysis, and chaos experiment tools through a single interface.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

Provides tools to search Splunk logs, inject test data, and send triage emails, enabling AI-driven incident investigation.

A comprehensive MCP server providing advanced Redmine project analytics, web automation via Playwright, and iTunes music integration. It enables detailed tracking of sprint metrics, bug counts, and team performance through a token-optimized tool architecture.

A dual-server MCP implementation with task-based AI processing using Google Gemini API, featuring tool integration, real-time monitoring dashboard, and extensible framework for custom AI workflows.

Enables AI agents and users to manage workspace files, monitor system metrics, take persistent notes, and retrieve weather data via MCP tools and resources.

Enables exploring and diagnosing a Redis instance from MCP clients with read-only safety, using SCAN instead of KEYS for safe key enumeration.

Agentic tool that looks for statistical variations in conversation structure and logs unusual events to a SQLite database. Built using the Model Context Protocol (MCP), this system is designed to be used with Claude Desktop or other MCP-compatible clients.

Deterministic agentic data firewall that secures, monitors, and controls AI agent interactions with data and software via MCP, preventing data leaks.

Provides AI agents with honest benchmark rankings (Agentic Memory Index and Agentic Search Index) for AI tools, plus graded checks and telemetry for x402 endpoints.

Detrix is an agentic debugger. Watch any variable at any line, zero code changes during debugging Local or cloud - same workflow for Docker containers and remote hosts Python, Go, Rust - observation points capture values without pausing, without restarting

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.

Enables read-only access to JavaMelody monitoring data from one or more Java web applications, covering HTTP/SQL/Spring counters, JVM and thread snapshots, and database reports. Users can query several configured microservices from a single server process and inspect what is slow or failing without opening the JavaMelody dashboard.

Enables querying Google Analytics 4 data through MCP tools for running reports, listing properties, getting real-time metrics, and exploring metadata.