What is Gemini Tool Agent

Gemini Tool Agent is a lightweight Python library designed for building tool-aware agents that can interact with tools and communicate with MCP servers using Google’s Gemini AI models.

Use cases

Use cases include saving notes to a database, managing task lists, and automating responses in chat applications where tool integration is necessary.

How to use

To use gemini-tool-agent, install it via pip, initialize an agent with your API key, define custom tools with input schemas, and process queries that may require tool usage.

Key features

Key features include tool-aware conversation handling, structured prompt processing, automatic context management, JSON response parsing, and conversation history tracking.

Where to use

Gemini Tool Agent can be used in various fields such as customer support, virtual assistants, and any application that requires structured interactions with tools and data.

Gemini Tool Agent

A lightweight, tool-aware Gemini agent to handle structured prompts and tool usage in conversations.

Overview

Gemini Tool Agent is a Python library that provides a simple interface for creating tool-aware agents powered by Google’s Gemini AI models. It enables developers to define custom tools with structured input schemas and seamlessly integrate them into conversational flows.

Features

• Tool-aware conversation handling
• Structured prompt processing
• Automatic context management
• JSON response parsing
• Conversation history tracking

Installation

pip install gemini-tool-agent

Requirements

• Python 3.8 or higher
• Google Generative AI Python SDK (google-genai >= 0.3.2)

Usage

from gemini_tool_agent.agent import Agent

# Initialize the agent with your API key
agent = Agent(key="your-api-key")

# Define your tools
agent.tools = [
    {
        "name": "save_note",
        "description": "Save a note to the database",
        "input_schema": {
            "title": "string",
            "content": "string"
        }
    }
]

# Process a query that might use tools
response = agent.process_query("Save a note about AI agents")
print(response)

Response Format

The agent returns a structured response in JSON format:

{
  "needs_tool": true,
  "tool_name": "save_note",
  "needs_direct_response": true,
  "direct_response_first": false,
  "reasoning": "The query explicitly asks to save a note, which requires the save_note tool",
  "direct_response": "AI agents are software entities that can perform tasks autonomously..."
}

Tool Parameter Extraction

After identifying that a tool needs to be used, you can extract parameters from the conversation:

# First process the query to determine if a tool is needed
response = agent.process_query("Save a note titled 'AI Agents' with content about machine learning")

# If a tool is needed, extract the parameters
if response.get("needs_tool", False):
    tool_name = response.get("tool_name")
    tool_params = agent.process_use_tool(tool_name)
    
    # Now you can use the extracted parameters to execute the tool
    print(tool_params)
    # Output: {'tool_name': 'save_note', 'input': {'title': 'AI Agents', 'content': '...'}}  
    #You can then execute the tool with the extracted parameters

Optimized Response Generation

The agent automatically handles large prompts for memory efficiency:

# For direct usage (normally used internally by the agent)
response_text = agent.generate_response(large_prompt)

# The method automatically optimizes prompts over 10,000 characters by:
# - Trimming conversation history to the most recent 15 lines when needed
# - Truncating large direct responses while preserving start and end content

Advanced Usage

You can access the conversation history:

# Get the conversation history
history = agent.history

License

MIT

Author

Paul Fruitful ([email protected])