"How to identify when runtime errors occur in a program" matching MCP servers:

A unified developer toolkit for AI-assisted workflows. Task timing, doc drift detection, env validation, secret scanning, port conflict resolution, AI context generation, and license auditing — one MCP server, one install.

A minimal MCP server with get_weather and create_ticket tools, used for testing MCP servers across protocol, unit, eval, transport, and auth layers.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Bridges Xcode and Cursor to provide real-time access to build errors, warnings, and debug output from Xcode's DerivedData, enabling automated error analysis and fixes directly within Cursor.

Enables building and flying rockets in Kerbal Space Program 1.x via MCP, providing editor, flight control, and real-time telemetry tools without requiring visual input.

MCP server that wraps the Apify Workplace Program Detector actor, taking a company domain and returning the employee programs it publishes, such as ERGs, DEI, wellbeing, parental leave, and volunteering.

An MCP server that makes AI agents speak a brief summary of every response out loud using TTS.

Enables AI agents to monitor and debug browser runtime errors, console logs, and page diagnostics in real time via a Chrome extension and local MCP server.

Provides AI coding agents with real-time visibility into local development runtime state, enabling them to tail application logs, inspect ports, monitor process metrics, and diagnose network errors.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

A local, evidence-driven MCP runtime and control plane for open-source maintainers that provides workspace-bounded tools including controlled file operations, command execution, validation primitives, durable execution records, and human review workflows via stdio and Streamable HTTP transports.

Provides a single public MCP endpoint that aggregates multiple product runtime APIs, enabling AI assistants to discover and invoke product operations through tools/list and tools/call.

An AI recipe recommendation server based on the MCP protocol, providing functions such as recipe query, classification filtering, intelligent dietary planning, and daily menu recommendation.

Enables AI agents to search, read, and traverse a local knowledge base of Markdown files using full-text search and relationship graph, reducing token usage.

Exposes a live application's runtime state to coding agents, including logs, DB schema, config, and feature flags, with read-only SQL and secret redaction.

Enables MCP clients to serve and query semantic models, providing tools for entity descriptions, metric lookups, context resolution, and operation validation for AI agents.

Persistent memory and pattern learning for AI coding assistants via MCP.

A high-performance MCP execution engine that runs tool code in isolated, resource-constrained sandboxes with context state persistence.

Enables agents to safely drive a simulated Franka Panda pick-and-place cell through MCP tools, with gated motion, emergency stop, structured errors, and audit logging.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.