"A platform for monitoring and analyzing system and application performance" 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 AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

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.

A comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.

A read-only MCP server that enables AI agents to act as GCP platform engineers, allowing them to investigate incidents, take inventory, and find cost-optimization opportunities in Google Cloud projects without mutating any infrastructure.

Enables comprehensive analysis of AWS Lambda performance including cold start tracking, memory utilization monitoring, cost analysis, and optimization recommendations. Provides real-time metrics, comparative analysis across multiple functions, and actionable insights to improve Lambda performance and reduce costs.

Enables MCP clients to inspect Oracle Integration Cloud integrations, connections, runtime instances, errors, and flow logs through natural-language queries.

An MCP server that provides access to Google Cloud Monitoring API, enabling interaction with cloud resources monitoring data through natural language commands.

Enables Managed Agent sessions to retrieve provider-specific alarms, metrics, and logs through MCP tools, supporting mock and AWS-backed monitoring data.

Read-only MCP server that exposes Kubernetes platform state (tenants, pods, SLOs, ArgoCD applications, chaos schedules, and catalog services) to AI agents, enabling natural language queries about cluster health and configuration.

Enables AI agents to interact with ServiceNow through MCP, providing schema inspection, record CRUD, attachments, audit, Flow Designer, and platform investigations with policy-guarded access.

Enables platform engineering workflows via GitHub Copilot, including creating repositories from templates and provisioning Azure deployment environments.

MCP server giving AI assistants read-only access to a Jamf fleet via the Jamf Platform API Gateway using OAuth2 client credentials, with tools for fleet overview, device queries, group membership, automation auditing, and passthrough requests.

MCP server for Azure resource management, AI Foundry, and Entra ID — inspect and operate Azure infrastructure through AI agents.

Enables DevOps operators to query and operate the Sigma Streaming Platform from any AI client such as Claude Desktop, Cursor, or Kiro.

Enables interaction with Mia-Platform Console through the Model Context Protocol, supporting machine-to-machine authentication and personal credentials for managing resources.

Connects to a GCP-hosted AOSP build VM via IAP tunnel, uploads APKs as privileged system apps, and builds system.img.

Enables AI assistants to query and manage Flowcore resources through a structured API.

Provides AI agents with real-time access to live Azure infrastructure, including AKS cluster health, resource management, policy validation, and Terraform analysis through a Model Context Protocol interface.

Exposes Power Platform Pipeline operations as MCP tools for Copilot Studio agents, enabling pipeline discovery, deployments, approvals, and configuration management.