"Java command reference and usage" matching MCP servers:

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.

Exposes a get_context_usage tool that reports raw token usage from session transcripts for Claude Code and OpenAI Codex CLI, enabling agents to check context size and branch behavior.

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.

Enables AI agents to search for reference images using natural language queries, aggregate results from multiple image sources, and interactively select, refine, and download images through tool calls.

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.

MCP server for profiling Java applications via JDK utilities (jcmd, jfr, jps). Enables AI assistants to diagnose performance, analyze threads, and inspect JFR recordings without manual CLI usage.

A local MCP server that exposes Blacksmith.sh's usage and cost data, enabling queries for CI spend through agents, alerts, or dashboards.

Decompiles Maven dependencies into readable Java source directly inside your AI agent.

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

A secure Model Context Protocol server that allows AI models to safely interact with Windows command-line functionality, enabling controlled execution of system commands, project creation, and system information retrieval.

MCP server that wraps Cursor Enterprise API to enable AI agents to answer questions about team spending, usage, and model adoption.

Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.

A high-performance MCP server that bridges AI agents with Java codebases, providing professional-grade Java language intelligence via Eclipse JDT.LS.

Extracts minimal, relevant code context from multiple programming languages while analyzing diffs and optimizing imports to reduce token usage for AI assistants. Supports TypeScript/JavaScript, Python, Go, and Rust with token-aware caching.

A Codex MCP server that provides low-token Java semantic navigation using source indexing and optionally JDT Language Server for enhanced symbol, references, and diagnostics.

Enables AI tools to analyze Java dependencies by scanning Maven projects, decompiling JAR files, and extracting detailed class information including methods, fields, and inheritance relationships. Solves the problem of AI hallucinations when generating code that calls external dependencies by providing accurate class structures through decompilation.

A self-hosted MCP server for building a reusable AI-video reference library. It stores named Elements as structured reference images and provides them to MCP-compatible agents via @tags.

MCP server that scans Maven project dependencies, decompiles Java class files, and provides class structure analysis to LLMs for accurate code generation.

MCP server that provides AI assistants with searchable access to MikroTik RouterOS documentation, including command reference, hardware specs, and video transcripts via SQLite FTS5 retrieval.