About whois
whois is a minimal MCP example for asking an AI about a person, with both an agent and a server. It can be run with Anthropic, OpenAI, Gemini, or a local Llama setup.
Key features
- 01Example person lookup
- 02Includes an MCP server and agent
- 03Supports Anthropic, OpenAI, Gemini, and Llama
Use cases
- 01Ask an AI about someone by name
- 02Test person queries with different models
MCP support
The project includes an MCP server and an agent that submits person queries.
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 READMEwhois
A minimal example of an MCP agent and server that can be used to ask an AI about who someone is.
Installation
Install dependencies with:
pip install -r requirements.txt
Environment Variables
Ensure these keys are set in your environment for secure API access:
export ANTHROPIC_API_KEY='your_anthropic_api_key'
export OPENAI_API_KEY='your_openai_api_key'
export GEMINI_API_KEY='your_gemini_api_key'
export LLAMA_API_URL='http://localhost:11434/api/generate' # For Ollama Llama3 model
Usage
- Run the Server:
uvicorn server:app --reload
- Run the Agent (example queries):
python agent.py "Albert Einstein" openai
python agent.py "Richard Feynman" anthropic
python agent.py "Nikola Tesla" llama
python agent.py "Stephen Hawking" gemini
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.