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MCP Tools Module

Overview

The mcp_tools module in the AgenticAI Framework provides integration with Model Context Protocol (MCP) tools, enabling AI agents to interact with external systems, APIs, and resources in a standardized way. It allows developers to extend agent capabilities by adding custom tools that can be invoked dynamically.

Key Classes and Functions

  • MCPTool — Base class for defining a new MCP tool.
  • ToolRegistry — Manages registration and discovery of available tools.
  • execute_tool(name, kwargs)** — Executes a registered tool by name.
  • list_tools() — Returns a list of all available tools.
  • load_tools_from_config(config_path) — Loads tool definitions from a configuration file.

Example Usage

from agenticaiframework.mcp_tools import MCPTool, ToolRegistry

# Define a custom tool
class WeatherTool(MCPTool):
    name = "get_weather"
    description = "Fetches weather information for a given city."

    def run(self, city: str):
        return f"Weather in {city}: Sunny, 25°C"

# Register the tool
registry = ToolRegistry()
registry.register(WeatherTool())

# Execute the tool
result = registry.execute_tool("get_weather", city="San Francisco")
print(result)

Use Cases

  • Integrating with external APIs (weather, finance, news, etc.).
  • Automating system operations (file management, database queries).
  • Extending AI agent capabilities with domain-specific tools.
  • Enabling dynamic tool discovery and execution.

Best Practices

  • Keep tool interfaces simple and well-documented.
  • Validate input parameters to prevent errors.
  • Use secure authentication for tools that access sensitive data.
  • Organize tools into logical categories for easier discovery.