# MCP Server for Gemini CLI

This repository contains an MCP (Multi-modal Conversational Platform) server built with `fastmcp` that extends the capabilities of a Gemini CLI agent. It provides specialized tools and prompts, allowing the Gemini agent to interact with external services like YouTube for transcript retrieval and to process information into structured formats.

## Features

The server exposes the following functionalities to a connected Gemini CLI agent:

### 1. `get_youtube_transcript` Tool

*   **Purpose:** Fetches the full transcript of a given YouTube video.
*   **Parameters:**
    *   `url_or_id` (string): The URL or unique ID of the YouTube video.
    *   `include_timestamps` (boolean, optional, default: `False`): If `True`, timestamps are included with each line of the transcript.
*   **Output:** Returns a JSON string containing the `video_id`, `language`, number of `snippets`, and the `transcript` content.
*   **Error Handling:** Gracefully handles cases where transcripts are disabled, not found, or the video is unavailable.

### 2. `youtube_transcript_to_notes` Prompt

*   **Purpose:** A detailed prompt designed to guide an AI agent in converting YouTube video transcripts into comprehensive, Markdown-formatted study notes.
*   **Instructions for AI:** The prompt provides a structured approach for the AI to:
    *   Infer a course name from the video title/description.
    *   Save notes to a specific file path (`./[course_name]/index.md`), ensuring existing notes are merged and enriched, not overwritten.
    *   Focus on explaining concepts thoroughly, including definitions, key ideas, examples, and real-world applications.
    *   Utilize Markdown for clear formatting (lists, tables, diagrams).
    *   Conclude with a "Key Takeaways" section.
    *   Return the entire updated Markdown file.

### 3. `search_google_prompt` Prompt

*   **Purpose:** A prompt that instructs an AI agent to use an external `search_google_tool` (assumed to be available to the AI) to perform web searches and format the results.
*   **Instructions for AI:** The prompt guides the AI to:
    *   Execute a search using the `search_google_tool` with a given query.
    *   Process the structured JSON results from the search API.
    *   Present the results in a well-formatted Markdown list.
    *   Save the formatted output to a Markdown file named `/opt/custom/arun-esh.github.io/docs/gemini/query_{query}_time_stamp.md`.

## Prerequisites

*   Python 3.12+
*   `pip` (Python package installer)
*   (Optional) Docker for containerized deployment

## Setup

You can set up and run the MCP server either locally or using Docker.

### Local Setup

1.  **Clone the repository:**
    ```bash
    git clone https://github.com/test-user/mcp-server.git # Replace with actual repo URL if different
    cd mcp-server
    ```

2.  **Create and activate a virtual environment:**
    ```bash
    python3 -m venv .venv
    source .venv/bin/activate
    ```

3.  **Install dependencies:**
    ```bash
    pip install -r requirements.txt
    ```

4.  **Run the server:**
    ```bash
    python main.py
    ```
    The server will start and be accessible at `http://0.0.0.0:8000/mcp`.

### Docker Setup

1.  **Build the Docker image:**
    ```bash
    docker build -t mcp-server-image .
    ```

2.  **Run the Docker container:**
    ```bash
    docker run -d -p 8081:8000 --name mcp-server-container mcp-server-image
    ```
    This command runs the server in a detached mode, mapping the container's port `8000` to port `8081` on your host machine. The server will be accessible at `http://localhost:8081/mcp`.

## Connecting to Gemini CLI

To enable your Gemini CLI agent to use this MCP server, you need to configure its `GEMINI_MCP_SERVER_URL`.

1.  **Install Gemini CLI:**
    If you haven't already, install the Gemini CLI (refer to its official documentation for the most up-to-date installation instructions).

2.  **Configure the server URL:**
    Set the `GEMINI_MCP_SERVER_URL` environment variable to point to your running MCP server.

    *   **For local setup:**
        ```bash
        export GEMINI_MCP_SERVER_URL="http://localhost:8000/mcp"
        ```
    *   **For Docker setup:**
        ```bash
        export GEMINI_MCP_SERVER_URL="http://localhost:8081/mcp"
        ```
    You can also add this line to your shell's profile file (e.g., `.bashrc`, `.zshrc`) to make it persistent.

    Alternatively, you can configure your Gemini CLI using a `settings.json` file (typically located in `~/.gemini/settings.json` or a similar configuration directory). This provides a more persistent way to manage your MCP server connections.

    **Example `settings.json`:**
    ```json
    {
      "security": {
        "auth": {
          "selectedType": "oauth-personal"
        }
      },
      "mcpServers": {
        "gemini-mcp-server": {
          "httpUrl": "http://127.0.0.1:8081/mcp/"
        }
      }
    }
    ```

## Usage Examples (Conceptual)

Once connected, your Gemini CLI agent can leverage the server's functionalities. For instance:

*   **Fetching a YouTube transcript:** An AI might call the `get_youtube_transcript` tool with a video URL.
*   **Generating study notes:** An AI could be prompted with `youtube_transcript_to_notes("https://www.youtube.com/watch?v=example")` to generate notes from a video.
*   **Performing a Google search:** An AI could use `search_google_prompt("latest AI research")` to get formatted search results.

Refer to the Gemini CLI documentation for specific commands and interaction patterns with MCP servers.