"Resources or information related to coding or programming" matching MCP servers:

Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.

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

Integrates Monti APM with the Model Context Protocol to provide AI assistants with access to Meteor application performance monitoring data. It enables users to monitor system metrics, analyze method execution traces, and track application errors through natural language.

Compares AI coding tool spend before and after a migration -- Cursor, Claude Code, GitHub Copilot, OpenCode, Codex CLI -- via an MCP server, real numbers from each vendor's own API or local logs.

Enables coding agents to read performance findings from sixty, get detailed evidence for each finding, and close or resolve them with descriptions, while also checking service telemetry and providing installation instructions.

Enables read-only inspection of local coding-agent sessions across Claude Code, OpenCode, Cursor, and Codex, including running status, topics, todos, and transcripts, without writing or sending messages.

Provides a set of read-only Kubernetes functions via an MCP server, enabling interaction with Kubernetes clusters through agents or coding assistants like GitHub Copilot.

Enables observation-only system inspection through MCP, exposing system identity, resources, process summary, and network summary without any mutation or command execution.

Transparent Go proxy that intercepts, signs, rate-limits, redacts, and audits all MCP JSON-RPC tool calls without modifying client or server. Stores to JSONL or SQLite with HMAC-SHA256 signatures.

Enables diagnosing Kubernetes clusters without creating or changing cluster objects, with tools to inspect health, workloads, events, storage, RBAC, custom resources, logs, and configuration.

An MCP server that enables coding agents to log their work, decisions, test runs, PRs, and progress as structured events, either to a local JSONL file or an HTTP endpoint.

Enables MCP-aware AI agents to query the local machine's health over stdio, retrieving CPU, memory, disk, and load-average metrics along with boot time and the top processes by CPU usage. It gives coding agents a structured, protocol-standard way to answer questions like whether it is safe to run a build or what is currently consuming resources.

Enables coding agents to query, compare, and audit local profiler traces, benchmarks, memory captures, and execution evidence without uploading code or data, using CLI and MCP interfaces.

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 coding agents to read a replay link or a gn-tracing-*.zip package, surfacing console errors with source-mapped stacks, failed requests, and the user timeline.

Enables LLMs to analyze logs by extracting patterns, redacting secrets, and providing token-efficient summaries from files, Docker containers, or journald.

Query Prometheus from CLI or any MCP client, with retries, caching, and an optional metrics exporter.

Enables operators and MCP clients to perform read-only, multi-cluster Kubernetes diagnostics by correlating deployment inventory, approved Prometheus metrics, firing alerts, and reusable troubleshooting skills without executing cluster commands or modifying workloads. It exposes tools, resources, and prompts for deployment health checks, metric queries, alert inspection, and skill discovery.

Enables AI assistants to interact with tshark and related command-line tools for live packet capture, PCAP analysis, filtering, stream following, and capture file merging.

MCP servers for real-time development monitoring and interactive pair programming.