"Information about Gemini code" matching MCP servers:

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

Enables interaction with Google's Gemini AI models including file uploads, conversation management, and batch API processing for large-scale tasks at reduced costs. Supports multiple Gemini models with advanced features like embeddings generation and automated workflow processing.

A lightweight MCP server bridging AI agents to Google's Gemini AI via official CLI

Executes Python code in isolated rootless containers while proxying MCP server tools, reducing context overhead by 95%+ and enabling complex multi-tool workflows through sandboxed code execution.

Enables asynchronous and parallel execution of Gemini CLI tasks within Claude Code, allowing background task management and multi-instance parallelism.

Enables Claude Code to delegate tasks to OpenAI's Codex CLI (GPT-5.4) with structured execution traces, parallel execution, session persistence, and adversarial code review.

Model Context Protocol (MCP) server implementation that enables Claude Desktop to interact with Google's Gemini AI models.

A tool that allows Claude Code to use Gemini AI as an MCP server, leveraging Gemini's large context window for analyzing large files while saving Claude Code tokens.

A TypeScript-based MCP server that provides tools to interact with local Codex and Gemini CLIs via stdio transport. It enables users to execute prompts through the ask_codex and ask_gemini tools, supporting custom models and timeout configurations.

Pairs Claude Code with Google's Gemini AI for complementary code analysis, enabling intelligent routing where Claude handles local-context operations while Gemini leverages its 1M token context for distributed system debugging and long-trace analysis.

Token-optimized MCP server that integrates Google Gemini CLI for AI-powered coding assistance with 43.8% token savings through progressive disclosure, supporting multi-turn conversations and multiple Gemini models.

A Windows-compatible Model Context Protocol server that enables AI assistants to interact with Google's Gemini CLI, supporting file analysis, large context windows, and safe code execution.

A Model Context Protocol (MCP) server for integrating Google's Gemini 3 models with Claude Code, enabling powerful collaboration between both AI systems.

Exposes Gemini CLI's built-in tools and external MCP proxies through a unified MCP endpoint while providing an OpenAI-compatible API for Gemini models. It features configurable security modes to safely manage file system operations, web searches, and shell command execution.

Offloads expensive code reading, editing, and checking tasks to DeepSeek through Claude Code, saving Codex main-thread tokens by 40-60%.

Enables comprehensive codebase analysis using Google's Gemini AI through CLI integration. Provides architectural reviews and targeted code analysis with code2prompt integration for efficient context extraction.

Executes TypeScript code in a secure sandbox with IPC-bridged access to MCP tools like filesystem operations and network requests.

A Model Context Protocol server that enables Claude to interact with Google's Gemini AI models, allowing users to ask Gemini questions directly from Claude.

Enables safe execution of Python code with AI assistance and integration with Model Context Protocol tools.

A Code Mode MCP server for the e2b API, giving agents three tools (search, execute_read, execute_write) to write and run JavaScript functions inside sandboxed environments for API discovery and calls.

A Python-based MCP server that enables integration of Gemini AI models with MCP-compatible applications like Cursor/Claude, allowing for interaction with Gemini APIs through the Model Context Protocol.

Enables AI agents to write and execute Python code in an isolated sandbox that can orchestrate multiple MCP tool calls, reducing context window bloat and improving efficiency for complex workflows.

Composes arbitrary MCP tools using JavaScript, enabling discovery, composition, and execution of other MCP servers through a single 'exec' tool.

Enables running one Codex CLI task at a time in an isolated temporary folder, returning Codex's answer and a manifest of produced files for manual review and selective copying.

An MCP bridge that lets Codex supervise Reasonix as an implementation worker in an isolated Git worktree, ensuring safe delegation with scoped writes and sandboxed execution.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

An MCP server for running Claude Code and the Codex CLI as a pair: Claude drives, and hands self-contained tasks to Codex as background jobs.

Enables AI coding assistants to browse Unreal Python API documentation, search classes and functions, and execute Python code in Unreal Editor.

Enables coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.