About Flex MCP Agent
Flex MCP Agent is a structured chat agent for handling requests involving weather data, Confluence, and email. It is intended for users who configure MCP server connections and want one agent to carry out these tasks from user input.
Key features
- 01Fetch current weather for coordinates
- 02Send email through SendGrid
- 03Interact with Confluence tools
- 04Configure server URLs and transports
Use cases
- 01Retrieve weather for specified coordinates
- 02Send an email from an agent request
- 03Use Confluence tools during a conversation
MCP support
The agent uses MCP to connect with weather, email, and Confluence servers. Its project includes weather and email server scripts and can be configured with server URLs and transports.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEFlex MCP Agent
Agent that integrates weather data retrieval, Confluence interaction and email sending functionalities. It uses a structured chat agent to interact with users and perform tasks based on user input. The project leverages the Model Context Protocol (MCP) to standardize interactions between the agent and various data sources.
Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to large language models (LLMs). It acts like a USB-C port for AI applications, providing a standardized way to connect AI models to various data sources and tools. This protocol enables secure, two-way connections between data sources and AI-powered tools, facilitating seamless integration and interaction.
Project Structure
- mcp_weather_server.py: A server that provides weather data using the Open-Meteo API.
- mcp_email_server.py: A server that sends emails using the SendGrid API.
- mcp_agent.py: The main agent script that initializes the agent, loads configurations, and handles user input.
- prompt.py: Contains the system prompt for the agent, guiding its behavior and responses.
- config.example.json: An example configuration file for setting up server URLs and transports.
- requirements.txt: Lists the Python dependencies required for the project.
- .env.example: An example environment file for setting up necessary environment variables.
Setup Instructions
-
Clone the Repository
git clone [email protected]:MahithChigurupati/Flex-MCP-Agent.git cd Flex-MCP-Agent -
Create a Virtual Environment
python3 -m venv .venv source .venv/bin/activate -
Install Dependencies
pip install -r requirements.txt -
Configuration
- Copy
config.example.jsontoconfig.jsonand update the URLs and transport methods as needed. - Copy
.env.exampleto.envand fill in the required environment variables, such asSENDGRID_API_KEYandFROM_EMAIL.
- Copy
-
Running the Servers
-
Start the weather server:
python mcp_weather_server.py -
Start the email server:
python mcp_email_server.py -
For the Confluence MCP server, please check my other Git repository to clone and set it up.
-
-
Running the Agent
python mcp_agent.py
Usage
- Weather Data: The agent can fetch current weather data for specified coordinates.
- Email Sending: The agent can send emails using the SendGrid service.
- Confluence Integration: The agent can interact with Confluence using available tools.
Environment Variables
SENDGRID_API_KEY: API key for SendGrid.FROM_EMAIL: The email address from which emails will be sent.OPENAI_MODEL: The model name for OpenAI's language model.
Dependencies
The project requires the following Python packages:
langchain_mcp_adapterslangchain_openailangchainpython-dotenvsendgrid
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.