# [Your MCP Server Name]

<!-- Optional: Add a logo, banner, or badges here -->
<!-- Example badges:
![Python](https://img.shields.io/badge/python-3.11+-blue.svg)
![License](https://img.shields.io/badge/license-MIT-green.svg)
-->

## Project Overview

<!-- Replace this with a clear, concise description of what your MCP server does -->
[Provide a 2-4 sentence description of your MCP server. Explain what problem it solves, what domain it targets, and what makes it useful. For example: "This MCP server provides intelligent research assistance for academic writing. It helps users discover relevant papers, summarize key findings, and generate literature review sections with proper citations."]

## Demo

<!-- Optional but highly recommended: Link to a demo video -->
[If you created a demo video, link it here. Example: "🎥 [Watch Demo Video](https://youtube.com/...)" ]

## Architecture & System Design

<!-- Optional but recommended: Include architecture diagrams or explanations -->
[If you have architecture diagrams, include them here]

[Provide a detailed description (at least 250-500 words) of your system's architecture and design. Explain how your system is structured, how the components interact with each other, key design decisions you made, and why you chose this particular architecture. Consider addressing:
- Overall system structure and component organization
- Data flow between components
- Integration patterns (e.g., how MCP tools, prompts, and resources work together)
- External dependencies and APIs
- Scalability and performance considerations]

## Project Structure

Your project should follow a clear, organized structure. While you don't need to follow the exact structure shown below, having a well-organized codebase is essential for maintainability and testing.

### Recommended Structure (Reference)

Here's a recommended structure similar to the Nova project from the course:

- `src/server.py`: The main entry point that creates a `FastMCP` app instance and registers the routers.
- `src/routers/`: Contains `tools.py`, `resources.py`, and `prompts.py` that import implementations and register them with FastMCP using decorators.
- `src/tools/`, `src/resources/`, `src/prompts/`: Directories containing the business logic for each MCP capability.
- `src/app/`: Functions implementing your core business logic.
- `src/utils/`: Utility functions and helper code.
- `src/config/settings.py`: Uses `pydantic-settings` to manage configurations such as server name, version, API keys, and model choices.

### Your Project Structure

[Document your actual project structure here. Use headers (###, ####) to organize different parts of your codebase.]

### Dataset Sources

<!-- Optional: Document any custom datasets used -->
[If your project uses any custom datasets, document them here. Include information about:
- Dataset name and source
- Where it's stored in the project (e.g., `data/` directory)
- Format (CSV, JSON, etc.)
- Any preprocessing steps required
- License or attribution if applicable]

## Features

This section lists all the features implemented in this MCP server project to meet the course certification requirements.

### Mandory Features

**1. MCP Server Built in Python using FastMCP**
- [Explain how you implemented your MCP server with FastMCP. Example: "The server is implemented in `src/server.py` using the FastMCP framework."]

**2. At Least 1 MCP Tool**
- [List your MCP tool(s) and their location(s). Example: "An MCP tool is implemented in `src/tools.py`: `search_papers` (searches academic databases and returns relevant results)."]

**3. At Least 1 MCP Prompt with User Feedback Workflow**
- [Describe your agentic prompt and where user feedback is integrated. Example: "The `research_assistant` prompt is defined in `src/prompts.py`. It orchestrates a workflow where the agent first searches for relevant papers, presents a list to the user for selection (user feedback step), then generates summaries and citations for the selected papers."]

### Custom Features (Minimum 4 Required)

[Copy the descriptions of the custom features you implemented from the lesson. For each feature, list the relevant files that demonstrate its implementation. You must implement and document at least 4 custom features. Add as many as you implemented.]

**1. [Custom Feature Name from Lesson]**
- **Description:** [Copy the full description from the lesson's custom features list]
- **Implementation:** [Explain where/how this is implemented in your project. Example: "Implemented in `src/video_tools.py` and `src/multimodal.py`. The `analyze_image` tool uses GPT-4 Vision to extract text and information from research paper figures."]

**2. [Custom Feature Name from Lesson]**
- **Description:** [Copy the full description from the lesson]
- **Implementation:** [Your explanation]

...

[Add any other custom features you implemented]

## Setup Instructions

### Prerequisites

- [Python version. E.g. "Python 3.11 or higher"]
- [uv](https://github.com/astral-sh/uv) for dependency management
- [Any other prerequisites, e.g., Docker, specific system packages, etc.]

### Installation

1. Clone the repository:
   ```bash
   git clone https://github.com/[your-username]/[your-repo-name].git
   cd [your-repo-name]
   ```

2. Install dependencies using `uv`:
   ```bash
   uv sync
   ```

3. Set up environment variables:
   - Copy `.env.example` to `.env`:
     ```bash
     cp .env.example .env
     ```
   - Fill in the required API keys and configuration values (see [Environment Variables](#environment-variables) section below).

### Environment Variables

A `.env.example` file is provided in the repository. Copy it to `.env` and fill in your actual values:

```bash
# Required API Keys
[API_KEY_NAME]=your-api-key-here  # Get from: [URL where to obtain the key, e.g., https://platform.openai.com/api-keys]
# Example: OPENAI_API_KEY=sk-...  # Get from: https://platform.openai.com/api-keys

[ANOTHER_API_KEY]=your-api-key-here  # Get from: [URL or description]

# Optional API Keys (if using optional features)
[OPTIONAL_KEY]=your-optional-key  # Get from: [URL or description]
```

**Important:** 
- Never commit your `.env` file to the repository. Add it to the `.gitignore` file.
- Commit the `.env.example` file with placeholder values to help others set up the project.

## Running the Server

### As a Command-Line Tool

To start the MCP server locally:

```bash
uv run [your-server-main-file]
```

<!-- If you have a specific entry point, document it here -->

### Example Queries

Here are some example queries to test your MCP server:

```bash
# Example 1: [Tool/Function Name]
[Provide a concrete example query or command that demonstrates how to use one of your tools]

# Example 2: [Another Tool/Function Name]
[Provide another example]

# Example 3: [Prompt Name]
[Provide an example of how to trigger your agentic prompt]
```

[Add as many examples as needed to cover your main tools and prompts. Make these copy-pasteable for easy testing.]

### Other Commands (Optional)

<!-- Optional: Document any additional scripts or commands -->
[If you have additional commands such as evaluation scripts, data processing scripts, or other utilities, document them here. For example:

```bash
# Run AI evaluations
uv run python scripts/run_evals.py

# Process dataset
uv run python scripts/process_data.py
```
]

### Connecting from an MCP Client

To use this MCP server with any MCP client (e.g., Cursor, Claude Desktop, or others), add the following configuration to your project-level MCP settings file (e.g. `.cursor/mcp.json`):

```json
{
    "mcpServers": {
        "[your-server-name]": {
            "command": "uv",
            "args": [
                "run",
                "python",
                "-m",
                "[your.module.path]"
            ],
            "cwd": "${workspaceFolder}",
            "env": {
                "ENV_FILE_PATH": "${workspaceFolder}/.env"
            }
        }
    }
}
```

**Configuration Notes:**
- Replace `[your-server-name]` with your chosen server name
- Replace `[your.module.path]` with the Python module path to your server (e.g., `my_agent.server`)
- This configuration uses **relative paths** and **project-level scope**, making it safe to commit to git
- Environment variables are loaded from your `.env` file via `ENV_FILE_PATH`
- The `${workspaceFolder}` variable automatically resolves to your project root

**Important:** This configuration file can and should be committed to your repository, making it easy for others to test your MCP server.

[If you implemented authentication, document it here.]

## Testing

<!-- If you implemented tests, document how to run them -->
[If you implemented tests, explain how to run them, e.g., "Run `uv run pytest` to execute the test suite."]

## Troubleshooting

<!-- Optional: Include common issues and their solutions -->

### Issue: [Common Problem]
**Solution:** [How to fix it]

### Issue: [Another Problem]
**Solution:** [How to fix it]

## Contributing

<!-- Optional: Include if you want others to contribute -->
[If you want to allow contributions, add guidelines here. Otherwise, you can remove this section.]

## License

[Specify the license for your project, e.g., "This project is licensed under the MIT License - see the LICENSE file for details."]