Agent Mcp Framework
What is Agent Mcp Framework
The agent-mcp-framework is a project designed to facilitate the development and deployment of applications that interact with the MCP Server, providing a structured way to manage API calls and environment configurations.
Use cases
Use cases for the agent-mcp-framework include building applications that utilize machine learning models, automating data retrieval from APIs, and developing tools for data processing and analysis.
How to use
To use the agent-mcp-framework, first install the required dependencies by running ‘pip install -r requirements.txt’. Next, create a .env file in the project root directory and fill in the necessary environment variables such as GOOGLE_API_KEY, GOOGLE_CSE_ID, MODEL_API_KEY, MODEL_BASE_URL, and MCP_TOOL_PATH. Finally, start the application by executing the cli.py file.
Key features
Key features of the agent-mcp-framework include easy dependency management, environment variable configuration, and a command-line interface for seamless interaction with the MCP Server.
Where to use
The agent-mcp-framework can be used in various fields such as software development, data analysis, and machine learning, particularly in projects that require integration with the MCP Server.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Agent Mcp Framework
The agent-mcp-framework is a project designed to facilitate the development and deployment of applications that interact with the MCP Server, providing a structured way to manage API calls and environment configurations.
Use cases
Use cases for the agent-mcp-framework include building applications that utilize machine learning models, automating data retrieval from APIs, and developing tools for data processing and analysis.
How to use
To use the agent-mcp-framework, first install the required dependencies by running ‘pip install -r requirements.txt’. Next, create a .env file in the project root directory and fill in the necessary environment variables such as GOOGLE_API_KEY, GOOGLE_CSE_ID, MODEL_API_KEY, MODEL_BASE_URL, and MCP_TOOL_PATH. Finally, start the application by executing the cli.py file.
Key features
Key features of the agent-mcp-framework include easy dependency management, environment variable configuration, and a command-line interface for seamless interaction with the MCP Server.
Where to use
The agent-mcp-framework can be used in various fields such as software development, data analysis, and machine learning, particularly in projects that require integration with the MCP Server.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
如何启动项目
第一步：依赖安装
pip install -r requirements.txt
第二步：在项目根目录下创建.env文件，补充下述环境变量
GOOGLE_API_KEY="" GOOGLE_CSE_ID="" BING_API_KEY="" BING_SEARCH_URL="https://api.bing.microsoft.com/v7.0/search" MODEL_API_KEY ="" MODEL_BASE_URL="" MCP_TOOL_PATH ="tool.py path url " MODEL_NAME ="gpt-4o-mini" MODEL_TYPE = "openai"
第三步：启动cli.py文件即可
问题实例1（预计调用google_search或者bing_search工具，这两个工具需要配置好代理）： 小米su7怎么样？
问题实例2（预计不调用工具）： 你好啊
问题实例3（预计调用组合工具链）：帮我预定2025年5月6日武汉到广州的机票，并且帮我看看那趟航班的信息和两地的天气
问题实例4（对话记忆功能）：
组合问题：
问题1: 今天是2025年5月6日，我在武汉，帮我看看现在武汉天气怎么样
问题2: 帮我预定一趟去广东的飞机
问题3: 给我展示一下这趟航班的信息
2025年4月份迭代计划
（1）初步搭建mcp客户端 （☑️）
（2）替换统一调用模型（☑️）
（3）调研langchain的graph工作流节点（☑️）
2025年5月份迭代计划
（1）调研开源项目intel opea的微服务组成架构，改写成本项目的微服务架构，并结合langgraph进行统一微服务节点工作流编排
（2）fastapi微服务架构搭建
（3）langgraph工作流编排搭建
（4）提示词使用规范化（langchain v3）（☑️）
（5）新增聊天历史功能 (☑️)
2025年6月份迭代计划
待定
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
