Gemini Terminal Agent

Name: Gemini Terminal Agent
Author: Nghiauet

@Nghiauet

代码仓库

关于 Gemini Terminal Agent

create agent with mcp server

#mcp-agent #gemini-terminal-agent #terminal-agent

基本信息

分类

AI 与智能体

许可证

MIT license

运行时

python

传输方式

stdio

发布者

Nghiauet

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-agent-nghiauet": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Gemini Terminal Agent?

Gemini Terminal Agent is a terminal-based agent that uses Google’s Gemini model and integrates real‑time web search via Google Custom Search Engine. It is intended for developers and power users who want a conversational AI interface directly in their terminal.

How to use Gemini Terminal Agent?

Install Python 3.9+, clone the repository, create a virtual environment, install dependencies, and add your API keys to a .env file. Run python main.py to start the agent. Type prompts or questions, and use commands such as help, clear, and exit.

Key features of Gemini Terminal Agent

Conversational AI interface using Google’s Gemini models
Web search integration for up‑to‑date information
Conversation history maintained across turns
Advanced search options (domain filtering, exclusions, time range)
Clean, modular architecture that is easy to extend

Use cases of Gemini Terminal Agent

Ask questions and receive Gemini‑powered answers from the terminal
Perform real‑time web searches without leaving the command line
Maintain multi‑turn context for deeper technical discussions

FAQ from Gemini Terminal Agent

What is the difference between Gemini Terminal Agent and the Gemini web UI?

Gemini Terminal Agent runs locally in your terminal and adds built‑in web search capabilities, which the standard Gemini web interface does not provide.

What are the runtime dependencies?

Python 3.9 or higher, a Google Gemini API key, and a Google Custom Search Engine (CSE) API key with its corresponding CSE ID.

Where is conversation history stored?

History is kept in memory for the duration of the session. It can be cleared by typing the clear command.

What search features are available?

The agent supports both basic and advanced web search, including domain filtering, site exclusion, and time‑range options.

How do I configure the model and search settings?

All settings are managed through the .env file, where you can set the model name, maximum concurrent requests, timeouts, and cache TTL.

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