"Aufgaben von Claude zu Gemini zuweisen, um Token zu sparen" matching MCP servers:

Enables Claude Code to call Gemini models through an OpenAI-compatible API, providing tools for deep analysis, brainstorming, code review, and general queries using Gemini's capabilities.

Enables seamless real-time collaboration between Claude Code and the Gemini CLI for discussing ideas and refining solutions. It provides tools to initiate collaborative sessions, consult Gemini with context preservation, and access conversation history.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

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

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.

Connects Claude Code with Google's Gemini AI, allowing users to ask Gemini questions, get code reviews, and brainstorm ideas directly within Claude Code.

Bridges Claude Code and Google's Gemini AI models to enable AI-to-AI collaboration for code reviews, brainstorming, and direct questions.

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.

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

High-performance Model Context Protocol (MCP) server connecting Claude Code and AI agents to Google Gemini 3.8 Flash, Gemini 3.1 Pro, reasoning effort, and image generation.

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 lightweight MCP server that enables AI coding assistants to interact with Google's Gemini AI through the official CLI.

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.

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

A universal MCP server that provides AI-powered code review and analysis for any programming language using Google's Gemini CLI.

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