# 📚 MCP Documentation Server (Version 1)

This project is an **MCP (Model Context Protocol) Server** designed to help fetch and search the latest documentation for libraries such as **`LangChain`**, **`OpenAI`**, and **`LlamaIndex`**.  
It allows you to query documentation sources and retrieve clean, plain text using a combination of **`Google Serper API`** for search and **`BeautifulSoup`** for scraping.

⚡ This is **Version 1 (In Progress)** — future versions will expand with more features and libraries.

---

## 🚀 Features

- 🔍 Search official docs for supported libraries:
  - [LangChain](https://python.langchain.com/docs)
  - [OpenAI](https://platform.openai.com/docs)
  - [LlamaIndex](https://docs.llamaindex.ai/en/stable)
- 🌐 Fetch and clean documentation text directly from web pages.
- ⚙️ Exposed as an **MCP Tool** via `server.py`.
- ⏳ Handles timeouts gracefully to avoid breaking workflows.
- 🔑 Configurable using `.env` with your **Serper API Key**.

---

## 📂 Project Structure

```bash
documentation/
│── services/
│ │──── scrape.py 
│ │──── search.py 
│
│── tools/
│ │──── get_docs.py 
│
│──── config.py
│──── server.py
│──── pyproject.toml
│──── .env.example
│──── .gitignore
```

---

## ⚡ Setup & Installation

This project uses [**uv**](https://docs.astral.sh/uv/) for dependency management.  

### 1️⃣Clone the repository

```bash
git clone <your-repo-url>
cd documentation
```

### 2️⃣ Create a virtual environment

```bash
uv venv
```

### 3️⃣ Activate the virtual environment

Linux/macOS:
```bash
source .venv/bin/activate
```
Windows (PowerShell):
```bash
.venv\Scripts\Activate
```

### 4️⃣ Install dependencies

```bash
uv sync
```

---

## 🔑 Environment Variables

Copy .env.example → .env and set your Serper API key:
```bash
SERPER_API_KEY="your_api_key_here"
```
You can get an API key from [Serper.dev](https://serper.dev/)

---

## ▶️ Running the MCP Server

Start the server with:
```bash
uv run --with mcp mcp run server.py
```
This runs the MCP server over stdio, making it available as a tool.

---

## 🛠 How It Works

1. get_docs MCP tool
    - Input: query + library
    - Example: query="embedding models", library="openai"
    - Process:
        - Builds a site-specific query (site:platform.openai.com/docs embedding models)
        - Fetches results using Serper API
        - Scrapes and cleans text from found pages
    - Output: plain text documentation snippets
2. Can be integrated into other MCP-compatible apps for automated documentation search & retrieval.

---

## 📌 Notes

- This is Version 1 — focused on core functionality.
- Future improvements:
    - Support for more libraries/frameworks.
    - Improved result filtering & ranking.
    - Error handling & logging improvements.
    - Possible caching layer for repeated queries.

---

## 🐳 Docker Support

This project also provides a Dockerfile so you can containerize the MCP Documentation Server.

- Why Docker?
    - Consistency: run the server with the same environment on any machine.
    - Isolation: dependencies are fully contained, no need to pollute your local Python environment.
    - Portability: easy to share or deploy on servers/CI pipelines.
    - Caching: faster rebuilds thanks to Docker layer caching.

- When do you need it?
    - If you want to run the MCP server without installing uv or Python locally.
    - If you plan to deploy on a server or inside CI/CD pipelines.
    - If you want a reproducible environment across your team.

- How to use it?
    1. Build the image:
        ```bash
        docker build -t docs-mcp:dev -f Dockerfile .
        ```

    2. Run with your .env file (containing SERPER_API_KEY):
        ```bash
        docker run --rm -it --env-file .env docs-mcp:dev
        ```

    That’s it — the server will start inside the container and be available to any MCP-compatible client (e.g. Claude Desktop).

---

## 💻 Running Locally with Claude Desktop

You can also connect this MCP Documentation Server directly to Claude Desktop on your machine.

Steps:

1. Install Claude Desktop
    - Make sure Claude Desktop is already installed on your system.

2. Enable Developer Mode
    - Open Claude Desktop.
    - Go to Developer Mode.
    - Click Add Configuration. 
    This will generate a local file called:
    
        ```bash
        claude_desktop_config.json
        ```

3. Edit the Configuration File
    Open claude_desktop_config.json and add a new entry like this (adjust the paths to match your machine):
    ```json
    {
        "mcpServers": {
            "documentation": {
            "command": "C:\\Users\\Alawakey\\Desktop\\MCP Server\\documentation\\.venv\\Scripts\\python.exe",
            "args": ["C:\\Users\\Alawakey\\Desktop\\MCP Server\\documentation\\server.py"],
            "cwd": "C:\\Users\\Alawakey\\Desktop\\MCP Server\\documentation"
            }
        }
    }
    ```
    - `command`: path to your Python executable inside the virtual environment.
    - `args`: path to the `server.py` file.
    - `cwd`: working directory of your project.

4. Restart Claude Desktop
    - Fully close Claude Desktop and start it again.

5. Verify the MCP Server
    - In Claude Desktop, go to Search and Tools (near the text input).
    - You should see your MCP server listed under the name you provided (`documentation` in the example above).

6. Try some queries
    - Example prompts:
        ```bash
        how do i implement a chroma db in langchain?
        ```
        ```bash
        Use get_docs with query="ChatOpenAI" and library="openai"
        ```
    Claude will ask for permission the first time you use the tool. After granting it, the `get_docs` tool will fetch results from the documentation.

---