"Optimizing AI Model Thinking, Token Usage, and Context Size" matching MCP servers:

Enables coding agents to retrieve token-budgeted, source-hashed code context from explicitly registered local repositories via read-only MCP tools, reducing broad repository crawling while providing structure, symbols, and impact slices.

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

A production-ready MCP server template that connects LLMs and AI agents to external data, tools, and services with built-in OAuth 2.1 authentication, Redis-backed session management, and a modular tools engine.

Enables AI assistants to see which model IDs each provider's API has started listing since the last check, and to list the models a given provider currently serves with optional substring filtering, all without spending tokens.

A basic Python implementation of a Model Context Protocol server for educational purposes, using FastAPI and WebSockets.

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

Lets Claude look up your own Claude subscription usage limits — the same 5-hour and weekly percentages shown by /usage — mid-conversation, so it can pace long tasks and warn you before you hit a wall. It reads the local OAuth login, serves a throttled read-only snapshot (cached with graceful fallback), and exposes it as a single get_usage tool.

Enables analyzing and optimizing context window token usage by measuring, compressing, and eliminating waste from MCP tool definitions.

Enables Claude Code sessions to check account rate-limit headroom, local token spend, and live session status, with read-only MCP tools for limits, summaries, token usage, and session listing.

Provides AI assistants with structured access to metadata for 76+ AI models across Ollama, Claude, and OpenRouter, enabling capability queries, compatibility checks, model comparisons, and task-based recommendations.

Enables querying AI model lifecycle and deprecation data across OpenAI, Anthropic, Google Gemini, and Amazon Bedrock, including model status, sunset dates, and recommended replacements via MCP tools.

An MCP server that cuts AI coding-agent token usage by 60–80% via framework-aware context optimization.

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.

Provides systems thinking models as composable analysis lenses for architecture, infrastructure, DevOps, incident analysis, and technical decision-making.

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.

MCP server that reduces token consumption in AI coding assistants by up to 90% via structural reads, PreToolUse hooks, and tp-\* subagents.

Real-time Claude.ai subscription awareness for AI coding assistants. Surfaces live utilization, forecasts limits, gates expensive operations, and measures real per-task cost.

MCP plugin that alerts you when AI token usage is wasteful. Fires warnings, errors, and alerts on large outputs, verbose logs, and repetitive history, auto-suppressing noise to keep your context lean.

Enables MCP clients to query Kiro credit usage, including plan, used and remaining credits, reset date, and percentage used, through a read-only tool with optional cache refresh.

Enables AI coding agents to offload large file reads and boilerplate code generation to cheaper or local worker models through MCP, reducing token consumption and keeping context clean.

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.

Local-first dashboard + MCP server that parses Claude Code and Codex JSONL files into a SQLite cost / token tracker. Per-MCP and per-tool breakdown, session drill-down, dedup by request_id; never talks to vendor APIs

Forces host coding agents through a structured multi-step reasoning protocol, scaffolding, validating, and formatting their thinking in QUICK, STANDARD, or DEEP modes, plus independent refutation and self-consistency verification passes. It runs entirely locally over stdio or Streamable HTTP using bash with no LLM, API key, GPU, or inference cost.

Enables AI agents to monitor live token consumption and rate-limit windows across providers and assess whether expensive actions are justified before exhausting quotas, with optional budget guardrails.

Token usage tracker for OpenAI and Claude APIs with MCP (Model Context Protocol) support.

An MCP server that provides structural codebase indexing and surgical query tools to drastically reduce token usage through symbol-level searches and transitive impact analysis. It supports multiple languages and integrates with git to help AI agents understand code dependencies and the impact of changes in sub-millisecond time.

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