"Creating an application" matching MCP servers:

Enables AI agents to manage prompts and sessions via the ai-agent-logs API, with tools for health checks, login, listing/creating prompts, and retrieving sessions.

An MCP server that connects AI agents to Scouter APM, enabling natural-language queries against real-time application performance data.

Enables LLMs to query telemetry data via the Spyglass AI agent, providing intelligent insights about application performance, errors, and bottlenecks.

An MCP server for managing and monitoring Docker, Docker Compose, and Kubernetes environments alongside Azure Application Insights. It enables advanced log filtering, container lifecycle management, and querying of cloud application traces and metrics.

Read-only MCP server for investigating AWS ECS deployment and runtime issues, focusing on ECS Fargate services behind an Application Load Balancer.

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

Connects AI coding assistants to Shipbook for investigating issues using real application logs with error classification, log search, and session tracing.

Ships only the MCP server, not the recorder. Recording works by wrapping a process on the host — a local proxy, a PATH shim, a shadow git index. None of that survives a container boundary, so orca record inside here would watch an empty namespace. What does travel is the read side: the six MCP tools serve runs that already exist on disk, so a client mounts its .orca directory and r

Enables an AI assistant to diagnose a broken application on a local Kubernetes cluster by inspecting pods, events, and logs, with server-side guardrails, least-privilege RBAC, and an audit log. It can also restart deployments in allowed namespaces only after a dry-run preview and host approval.

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.

Transforms Elasticsearch into an AI-powered observability engine for analyzing logs, APM traces, and system metrics through natural language.

Enables MCP-compatible AI clients to search and retrieve Elasticsearch application logs through a provided search tool.

Enables AI assistants to interact with Monti APM for Meteor application performance monitoring.

An MCP server that provides AI assistants with direct access to application logs for on-demand searching, filtering, and analysis. It enables tools like Cursor to summarize log entries and identify errors within the development environment to streamline debugging.

A read-only MCP server that exposes Azure incident-investigation tools for a single App Service and its Application Insights telemetry, enabling an agent to correlate deployment history, platform metrics, and transaction traces.

A command-line tool for monitoring and analyzing New Relic application metrics using MCP.

Enables AI assistants to read and analyze Linux system logs—including systemd journal, syslog, kernel, application, and Docker container logs—for diagnostics and predictive analysis.

An MCP server that enables conversational interaction with Wazuh SIEM, allowing users to investigate alerts, hunt threats, tune false positives, edit rules, and run security actions via natural language.

Tracks a user's activity by trace ID from application logs stored in ClickHouse. Provides tools for log timeline, trace summaries, and search across services.

An MCP server for retrieving and filtering logs from Mezmo with a quota-conscious design and intelligent defaults. It enables users to discover and query logs by application, level, and custom time ranges to streamline debugging while minimizing API usage.