About mcp-servers
mcp-servers is a repository described as part of Conversive AI, a project intended to provide core generative AI services to the Conversive framework. The README gives setup and run instructions but does not specify the MCP servers or their capabilities.
Key features
- 01Conversive AI project setup instructions
- 02Python virtual environment setup
- 03Dependency installation steps
- 04Flask application launch instructions
Use cases
- 01Set up the Conversive AI project locally
- 02Install project dependencies
- 03Run the Flask application for testing
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 READMEREADME
This README documents steps necessary to get the application up and running.
What is this repository for?
This is Conversive AI project. The project is aimed to provide core Gen AI services to Conversive framework
- Version
- Learn Markdown
How do I get set up?
- Clone the Conversive AI repository
git clone https://git.txtbox.in:8080/smsmagicportal/conversive_ai.git
cd conversive-ai
- Create virtual environment
mkdir -p ~/virt
python3.9 -m venv ~/virt/conversive_ai
source ~/virt/conversive_ai/bin/activate
- Dependencies - install all required packages
pip install -U pip
pip install -r requirements.txt
- Configuration - Modify environment variables in
.envfile - Run the application
export FLASK_APP=src/main.py
export ENV="TEST"
export PYTHONPATH=$PWD
flask run
- Access application at http://127.0.0.1:5000
Contribution guidelines
- Writing tests
- Code review
- Other guidelines
Who do I talk to?
- Yashpal Meena [email protected]
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.