"A server for finding information about the % token" matching MCP servers:

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

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

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.

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.

A local MCP proxy that strips web pages to clean text, reducing token usage for AI agents by up to 99.6%.

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

MCP server for managing i18n translation files — gives your AI agent full control over your app's translations without dumping entire locale files into context.

An MCP server that intelligently filters and compresses tool outputs to reduce context window usage, saving up to 90% of tokens by removing noise such as passing tests and redundant information.

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.

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.

Enables structured learning with a verified loop: define goals as observable claims, learn through teach-lab-test-gate per claim, and get independently graded by an adversarial examiner to ensure genuine progress.

Provides sandboxed code execution and data processing for CSVs and logs to achieve over 95% token savings. It enables secure multi-language execution and progressive tool disclosure to optimize LLM context usage.

Quality gate for AI video renders: checks keyframes and clips for blur, flicker, frozen or black frames and look drift, and decides PASS / RETRY / SWITCH_TO_FALLBACK / HALT with a spend ledger.

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.

Automatically reduces token usage in Claude Code sessions using algorithmic optimizations like code compression, smart file reading, output summarization, and prompt rewriting, with no extra API calls or cost.

Enables AI coding agents to conserve tokens by enforcing rules like delegating heavy work to subagents, checkpointing before token limits, and grepping before reading files.

Reduces context window usage by 60-90% through intelligent caching, compression, and smart tool replacements for Claude Code and Claude Desktop.