Gemini Terminal Agent
@Nghiauet
About Gemini Terminal Agent
create agent with mcp server
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-agent-nghiauet": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}Tools
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Overview
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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