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

Enables AI agents to access and manage project guidelines, documentation, and context through a structured content system with template support and workflow management.

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.

A beginner-friendly Model Context Protocol (MCP) server that helps users understand MCP concepts, provides interactive examples, and lists available MCP servers. This server is designed to be a helpful companion for developers working with MCP. Also comes with a huge list of servers you can install.

MCP server that launches Mesen/MesenCE headlessly and exposes the emulator's debugger-face Lua API over MCP JSON-RPC, enabling AI agents to load ROMs, step frames, inspect memory/registers, manage breakpoints/watches, trace execution, and export Code/Data Logger maps for SNES, NES, PC Engine, and Game Boy Advance.

Provides tools for AI models to count characters and words in text, supporting English and other space-delimited languages.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

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.

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

Provides access to clean code guide documents and enables code quality inspection of external project files based on coding standards and best practices.

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.

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.

MCP server for estimating LLM token consumption and costs from project documents, code, and materials. Provides 8 tools including project budget planning, token/cost estimation, GitHub repo analysis, and pricing refresh.

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 reduces token usage by injecting graph-ranked repo maps, decision logs, and diff-only output into AI coding tool requests.

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

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

Token-safe code search for AI agents: queries the language-server index (clangd / Roslyn / tsserver / pyright) instead of grep and returns a token-capped file:line list — ~20x fewer tokens. Symbol-level editing + a grep→index rewrite hook. Local-only, no IDE.

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.