Extends large language model capabilities through tool invocation directly within the model's text output.
Sponsored
This repository provides a Flask-based implementation of the Model Context Protocol (MCP), enabling LLMs to leverage external tools through natural language interactions. Unlike traditional function calling, MCP places tool definitions directly within the context window, allowing the model to invoke tools using natural language responses, which are then parsed and executed. The server includes sample tools like weather and calculator, demonstrates conversation flow, and utilizes regex-based parsing for flexible tool invocation.
주요 기능
01Uses regex-based parsing for flexible text parsing of tool invocations.
02Includes sample weather and calculator tools with parameter validation.
03Maintains context across multiple interactions.
04Flask API provides REST endpoints for chat integration.
05Complete MCP implementation with parsing, execution, and response handling.
사용 사례
01Enhancing chatbot functionality with real-time information (e.g., weather data).
02Building conversational agents capable of interacting with external APIs.
03Enabling LLMs to perform calculations or data analysis tasks.
