"A guide to coding a monorepo" matching MCP servers:

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

Enables natural language queries of the Chrome UX Report API to fetch Core Web Vitals (LCP, INP, CLS, FCP, TTFB), compare form factors, benchmark pages against site averages, and analyze up to 40 weeks of performance trends.

Model Context Protocol server for Verlon AI that exposes gates, logs, recommendations, and experiments as MCP tools, enabling coding agents to inspect and manage AI infrastructure natively.

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.

BeamScope gives AI coding agents access to your running BEAM application through a resilient TCP architecture that survives app restarts.

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

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.

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

Provides a standardized MCP interface for querying Graylog logs, enabling AI agents to search, diagnose, and correlate runtime logs with code via configurable profiles.

Zero-config MCP server that gives AI coding assistants a real-time diagnostic snapshot of your local dev environment. Detects framework, running services, recent errors, git state, and provides a health diagnosis in one call.

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

Enables observability and governance for local LLMs via Ollama, including auditing model usage, scanning prompts for secrets/PII, and enforcing allow/deny policies.

MCP server for measuring, tracking, scoring, and improving AI agent reliability with tools for recording interactions, scoring reliability, analyzing failures, recommending improvements, generating audit reports, and checking MCP health.

Estimates the environmental footprint of your AI use — energy (kWh), miles driven, water used for cooling, and CO₂ — plus a prompt-efficiency score, working with any AI client by measuring token usage.

Enables MCP clients to interact with a local AI agent through Ollama, providing tools for checking weather, current time, and available models, while tracing calls to Langfuse.

Connects AI assistants to the Vectra AI security platform to enable intelligent analysis of threat detection data and automated incident response workflows. It allows users to investigate threats, take response actions, and generate security reports using natural language.

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

Perimeter security for your AI Infrastructure.

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-powered tools to the Confident AI platform for evaluating, observing, and iterating on AI quality, enabling cloud evaluations, dataset management, prompt versioning, production tracing, and human annotations directly from editors like Cursor and Claude Code.