the docker file

Dockerfile

@@ -0,0 +1,35 @@
+# syntax=docker/dockerfile:1.7
+FROM python:3.12-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    UV_PROJECT_ENV=.venv
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+WORKDIR /app
+
+# Install system tools + uv package manager
+RUN apt-get update && apt-get install -y --no-install-recommends curl ca-certificates \
+ && rm -rf /var/lib/apt/lists/* \
+ && curl -LsSf https://astral.sh/uv/install.sh | sh \
+ && ln -s /root/.local/bin/uv /usr/local/bin/uv
+
+# Copy dependency files first to leverage Docker layer caching
+COPY pyproject.toml uv.lock requirements.txt ./
+
+# Install dependencies in .venv with uv (cached layer)
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv sync --frozen --no-dev
+
+# Copy the rest of the source code
+COPY . .
+
+# Add virtual environment to PATH
+ENV PATH="/app/.venv/bin:${PATH}"
+
+# Environment variable to be set at runtime
+# ENV SERPER_API_KEY=your_key
+
+# Default command to start MCP server via stdio
+CMD ["uv","run","--with","mcp","mcp","run","server.py"]

README.md

@@ -1,3 +1,207 @@
-# MCP_SERCVER_SEARCH
+# 📚 MCP Documentation Server (Version 1)
 
-This MCP Server helps you search for some docs like langchain, lammaindex, only using the Query or Prompet
+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.
+
+---