"A guide to analyzing and fixing SEO on a webpage" 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.

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

Read-only MCP server for TrueNAS Scale debugging, providing k3s tools for kubectl operations and whitelisted midclt calls for system info, apps, and pools.

MCP server that diagnoses ML model regressions by correlating drift reports, eval runs, and deploy logs, providing evidence-cited incident reports through a set of investigation tools.

Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.

MCP server for On-Call Health burnout analysis. Connects AI assistants to your on-call data for workload insights.

A production-oriented reliability, benchmarking, and evaluation system that layers multi-version protocol negotiation, adaptive streaming, connection pooling, and resilience onto an MCP agent runtime.

Enables querying and analyzing Datadog logs through search, aggregation, and index listing.

A Model Context Protocol server that enables retrieving and analyzing Sentry issues, including stacktraces and debugging information.

Enables evidence-driven debugging of RTL testbench simulation failures by parsing logs and analyzing FSDB/VCD waveforms through a guided MCP workflow.

A JVM monitoring and control platform server based on Arthas that provides a Python interface for monitoring and analyzing Java processes with capabilities for thread analysis, memory monitoring, and performance diagnostics.

Monitor and manage Apache Airflow clusters through natural language queries via MCP tools: DAG inspection, task monitoring, health checks, and cluster analytics without API complexity. * Guide: https://call518.medium.com/mcp-airflow-api-a-model-context-protocol-mcp-server-for-apache-airflow-5dfdfb2

A specialized diagnostic framework that enables AI models to self-diagnose and fix MCP-related issues by analyzing logs, validating configurations, testing connections, and implementing solutions.

Enables querying and analyzing distributed traces from Jaeger, including service discovery, trace inspection, and performance analysis, through MCP tools.

Structural observability for AI conversations. Detects loops, stuck states, breakthroughs, and convergence across 17 channels without analyzing content.

MCP server for collecting and analyzing CLI/web server error logs. Enables watching log files/directories, parsing common error patterns, and querying/analyzing logs through natural language.

Enables AI to automatically diagnose bugs by querying logs, tracing call chains, and analyzing code across multiple log platforms like Elasticsearch and Loki.

A minimal guardrails framework for AI agents, acting as an MCP proxy to enforce policies, approvals, and audit logging on tool calls.

Connects LLMs to Google PageSpeed Insights to analyze web performance, accessibility, SEO, and best practices, enabling AI assistants to audit and improve any web page.