"A guide for reducing token count in AI requests" matching MCP servers:

A CDP-native MCP server that lets AI agents drive a real Chrome browser with deep network/console inspection, robust page interaction, and the ability to reverse-engineer a site's API flow into runnable code with dependency chaining and replay verification.

Provides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.

Orchestrates AI agents through structured markdown documents, enabling multi-agent workflows with automatic context injection and workflow management.

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

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 AI agents to obtain a compressed code context where the active file is fully included and dependency files are reduced to type signatures, interfaces, and exports, cutting token usage while preserving code shape.

Enables AI agents to compare design tokens from multiple sources, retrieve prior reconciliation runs, and get detailed per-conflict breakdowns with confidence factors and alias chains.

Enables AI coding assistants to execute shell commands locally, intercepting deterministic commands like git status and npm test before they reach the LLM, saving tokens and reducing latency.

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

MCP server for amazon.in — cheapest in-stock, best-value picks, and price history links.

Enables AI agents to search, watch, summarize, clip, and extract transcripts from YouTube videos, all without needing an API key or leaving the chat.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

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

A read-only MCP server that reports token usage by agents and models, and recommends the cheapest safe model and effort level for a given task, helping to reduce token waste.

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

The Token Metrics Model Context Protocol (MCP) server provides comprehensive cryptocurrency data, analytics, and insights through function calling. This server enables AI assistants and agents to access Token Metrics' powerful API for real-time crypto market data, trading signals, price predictions, and advanced analytics.

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

Provides historical OHLCV candlestick data for any token by CoinGecko ID or contract address, with daily, 4h, and 1h intervals, paid per call via x402 micropayments.

Guides users to official WCAG and W3C resources for learning web accessibility, without providing its own explanations.

Enables AI agents to manage n8n workflow automation instances through tools for workflow CRUD operations, execution monitoring, and webhook triggering. It facilitates programmatic interaction with n8n instances via the n8n API with AI-optimized descriptions and error handling.