"Creating files in Xcode development environment" matching MCP servers:

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

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

Enables LLMs to analyze and manage DBOS workflows, including introspection, workflow management, and authentication, to help debug applications in development or production.

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.

An MCP server that gives your AI assistant full awareness of your local dev environment — running processes, Docker containers, git state, open ports, log files, and more.

Local-first CLI and MCP server for turning Xcode Instruments artifacts into bounded, agent-sized evidence, with support for analyses like Time Profiler, Allocations, Network, and more.

Enables natural language Kubernetes operations, including smart resource queries, pod root-cause analysis, cross-environment diffs, and manifest generation.

MCP server providing read-only operational tools (logs, metrics, traces, service health, config) for troubleshooting an environment, with one exception for toggling chaos scenarios.

Enables AI assistants to record, analyze, and compare Xcode Instruments traces, detect performance bottlenecks and regressions, and provide optimization recommendations.

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.

Enables incident detection and analysis by identifying anomalies in metric time series and surfacing root-cause candidates and recommended actions. Supports both mock (synthetic) and VictoriaMetrics backends with identical MCP tool contracts for seamless development-to-production switching.

Enables an LLM client to scan Windows event log files (EVTX) for suspicious activity using Hayabusa, and browse its detection rules directly in conversation.

Monitors development server logs in real-time and provides Claude with immediate error notifications via Server-Sent Events. Intelligently parses TypeScript, Svelte, and Vite errors with severity classification and file correlation.

Enables uploading PyPTO swimlane files and querying task durations and dependencies for performance analysis with swimlane diagrams in Codex.

MCP server to start, monitor, search, and detect errors in logs from any project, even those without log files.

Real-time observability dashboard for monitoring AI agents in multi-agent development workflows, with live WebSocket updates of tool calls, sessions, and usage statistics.

Analyzes log files using AI-powered tools to detect errors, patterns, and anomalies through natural language queries in Cursor IDE.

MCP-native agent evaluation and observability server. Log traces, evaluate output quality with 12 built-in rules (PII detection, prompt injection, cost thresholds), and track agent costs. Real-time dashboard, OTel-compatible spans. Self-hosted, MIT licensed.

MCP server exposing rlg (RustLogs) log files as tools for on-call / SRE agent workflows. Three tools over JSON-RPC stdio — tail_log (last N events, filtered by level/target), filter_log (predicate filtering), summarize_errors (grouped error taxonomy). Pure Rust. MIT OR Apache-2.0.