---
title: "Project Setup"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Project Setup

Let's setup the baseline project that we will use to complete fundamentals of MCP module. It is implemented in 3 different flavours and we will use the `agents_sdk_cli_project` as our baseline project

## MCP Chat - Agents SDK CLI project

MCP Chat is a command-line interface application. The application supports document retrieval, command-based prompts, and extensible tool integrations via the MCP (Model Control Protocol) architecture.

## Prerequisites

- Python 3.9+
- Any Chat Completions LLM API Key and Provider (i.e: Gemini)

## Setup

### Step 1: Configure the environment variables

1. Create or edit the `.env` file in the project root and verify that the following variables are set correctly:

```
LLM_API_KEY=""  # Enter your GEMINI API secret key
LLM_CHAT_COMPLETION_URL="https://generativelanguage.googleapis.com/v1beta/openai/"
LLM_MODEL="gemini-2.0-flash"
```

### Step 2: Install dependencies

[uv](https://github.com/astral-sh/uv) is a fast Python package installer and resolver.

1. Install uv, if not already installed:

```bash
pip install uv
```

2. Create and activate a virtual environment:

```bash
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
```

3. Install dependencies:

```bash
uv sync
```

4. Start MCP Server:
```bash
uv run uvicorn mcp_server:mcp_app --reload
```

5. Run the project with ChatAgent in CLI

```bash
uv run main.py
```

6. Optionally start inspector

```bash
npx @modelcontextprotocol/inspector
```


## Usage

### Basic Interaction

Simply type your message and press Enter to chat with the model.

### Document Retrieval

Use the @ symbol followed by a document ID to include document content in your query:

```
> Tell me about @deposition.md
```

### Commands

Use the / prefix to execute commands defined in the MCP server:

```
> /summarize deposition.md
```

Commands will auto-complete when you press Tab.

## Development

### Adding New Documents

Edit the `mcp_server.py` file to add new documents to the `docs` dictionary.

### Implementing MCP Features

To fully implement the MCP features:

1. Complete the TODOs in `mcp_server.py`
2. Implement the missing functionality in `mcp_client.py`

### Linting and Typing Check

There are no lint or type checks implemented.
