"Executing terminal commands and retrieving log outputs" matching MCP servers:

An MCP server for tracking and managing AI command usage history using a PostgreSQL database. It enables users to log, search, and view statistics for various AI-related commands and their execution contexts.

Enables management of visible, interactive terminal sessions across platforms (macOS, Windows, Linux, WSL). Supports creating, executing commands, capturing output, and managing multiple terminal windows simultaneously.

An MCP server to run commands.

The logs of everything on one machine — app, build, GPU, containers, serial board, OS — interleaved in the order they happened, and readable by an agent. Read-only: it can tail, search and wait, and it cannot write, delete or configure anything.

Enables AI assistants to automatically inspect and analyze application runtime log files for debugging and troubleshooting. Supports monitoring multiple log directories simultaneously with tools for listing, reading, searching, and paginating through log files.

MCP server for log file analysis. Gives LLMs the ability to efficiently analyze large log files without loading them into context.

Enables querying git commit history to analyze when and why code changes happened, providing authorship context and diffs for specific modules.

Read-only MCP server for searching and aggregating logs from VMware Aria Operations for Logs (formerly vRealize Log Insight). Enables querying log events, aggregating with spike detection, and retrieving alert information.

Security-hardened MCP server that runs only allowlisted commands with no shell, jailed to a single directory, and bounded execution.

MCP server for semantic log ingestion, hybrid retrieval, and cleanup, enabling natural-language querying of log files with dense+BM25 retrieval and full provenance.

An MCP server for AI-powered log analysis that enables parsing, searching, and debugging across nine log formats directly within Claude. It features automated error extraction, sensitive data scanning, and streaming support for large log files.

A Python MCP server that reduces token usage by ~98% when working with log files by auto-detecting format and stripping noise to return only actionable signal.

Enables AI models to run interactive tmux terminal sessions that a human can watch and type into, with all output logged and split into searchable per-command regions so only needed lines enter context. Also guides models through unfamiliar tools by returning exit statuses and key output alongside advice on the tool being used.

Enables MCP-compatible coding agents to debug applications using runtime log data, by providing tools to start a local log-ingestion server, track debugging sessions and hypotheses, and correlate logs to specific executions.

An MCP server for creating and managing shared terminal progress bars from AI agents, enabling real-time tracking of parallel tasks.

Enables AI agents to open persistent interactive terminal sessions with network devices over SSH, Telnet, TCP console, or serial, write raw commands including nested ssh/telnet transitions, and read full output without requiring a particular prompt format. It supports multiple concurrent sessions and keeps credentials/secrets out of MCP arguments or audit logs.

A log analysis MCP server that enables tailing, searching, filtering, and summarizing logs from local files and Docker containers.

Enables MCP clients to observe and, when explicitly enabled, send constrained input to whitelisted tmux sessions, with support for local STDIO and loopback-only HTTP transport behind an authenticated tunnel.

Local + remote terminal interaction control MCP Server. Lets AI agents control interactive TUI programs the way a human would.

An MCP server that gives Jan AI terminal and code execution capabilities, enabling shell commands, multi-language code execution, file manipulation, and git operations locally.