"Information about terminal commands or usage" 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 AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

AI coding usage analytics — track spend, model usage, and costs across Cursor and Claude Code sessions.

Audits local Claude Code transcripts to show which connected MCP servers are actually used, idle, failing, or awaiting authorization, and recommends which to keep or disconnect.

Exposes rate-limit and usage data for AI coding agent CLIs as MCP tools, enabling querying of usage snapshots and reset times.

Enables retrieving real Claude subscription usage statistics, including the 5-hour session window, weekly limit, and per-model limits, from the same endpoint Claude Code uses.

An MCP server that exposes Claude Code's real-time rate limit usage so that an orchestrator can check remaining capacity before launching subtasks.

Monitors your Cursor usage and provides a conserve flag to the agent, helping you manage request quota by batching questions, preferring defaults, and cutting unnecessary confirmations.

Reports your Claude subscription usage (5-hour and weekly limits) with a forecast and velocity recommendation, using Claude Code's existing OAuth session without requiring an API key.

An MCP server that retrieves GitHub Copilot usage metrics and seat assignment data across Enterprise, Organization, and Team levels. It allows users to monitor code completions, chat activity, and active user counts through integrated tools.

A VSCode and Cursor extension that captures real-time terminal output and exposes it to AI assistants via the Model Context Protocol. It enables agents to proactively monitor logs, command execution, and errors without requiring manual copy-pasting from the user.

Exposes GitHub Copilot premium request usage as an MCP tool, providing a breakdown by model and cost.

Enables AI assistants to execute predefined developer commands and manage long-running processes through a JSON configuration file. Supports both one-shot commands and daemon processes with automatic process lifecycle management.

Gives the model visibility into the same 5-hour session, weekly, and per-model usage percentages shown in Claude Code, along with reset times. It also lets a project cap how much of the weekly limit may be spent via start/check/end budget tools that report OK, LOW, or OVER_BUDGET.

Developers: Search your zsh, bash, or fish shell history from Claude Code, Cline, Cursor, Zed, or any MCP client using tools like search_history (full-text with timestamp/CWD/exit code), recent_in_dir, failed_commands, and command_chains for multi-step sequences. Reindex after new activity. Local-only SQLite FTS5 with secrets redacted before storage.

Fetches and tracks Claude.ai usage data including session and weekly limits with automated daily logging and persistent session management. It allows users to monitor their usage history directly through Claude Code integrated tools.

Enables Claude Desktop to read terminal command output by capturing commands prefixed with 'cap' to a log file. Eliminates the need to copy-paste terminal errors and output when asking Claude for help.

Lets ChatGPT, Claude Desktop, Claude Code, and other MCP clients run an allowlisted set of local commands and read files from a chosen directory, so an assistant can analyse a codebase or trigger local generators (images, sprites, assets) without metered API tokens. The server is read-only and deny-by-default by default, with no shell, path containment, opt-in writes, and required auth when exposed over HTTP.

An MCP server that retrieves current GitHub Copilot usage data, including quotas, limits, and usage statistics. It allows AI agents to monitor premium interaction status and detailed account usage via raw or formatted summaries.

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