the docker file
هذا الالتزام موجود في:
35
Dockerfile
Normal file
35
Dockerfile
Normal file
@@ -0,0 +1,35 @@
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# syntax=docker/dockerfile:1.7
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FROM python:3.12-slim
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ENV PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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UV_PROJECT_ENV=.venv
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ENV DEBIAN_FRONTEND=noninteractive
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WORKDIR /app
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# Install system tools + uv package manager
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RUN apt-get update && apt-get install -y --no-install-recommends curl ca-certificates \
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&& rm -rf /var/lib/apt/lists/* \
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&& curl -LsSf https://astral.sh/uv/install.sh | sh \
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&& ln -s /root/.local/bin/uv /usr/local/bin/uv
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# Copy dependency files first to leverage Docker layer caching
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COPY pyproject.toml uv.lock requirements.txt ./
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# Install dependencies in .venv with uv (cached layer)
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --frozen --no-dev
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# Copy the rest of the source code
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COPY . .
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# Add virtual environment to PATH
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ENV PATH="/app/.venv/bin:${PATH}"
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# Environment variable to be set at runtime
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# ENV SERPER_API_KEY=your_key
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# Default command to start MCP server via stdio
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CMD ["uv","run","--with","mcp","mcp","run","server.py"]
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208
README.md
208
README.md
@@ -1,3 +1,207 @@
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# MCP_SERCVER_SEARCH
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# 📚 MCP Documentation Server (Version 1)
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This MCP Server helps you search for some docs like langchain, lammaindex, only using the Query or Prompet
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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`**.
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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.
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⚡ This is **Version 1 (In Progress)** — future versions will expand with more features and libraries.
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---
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## 🚀 Features
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- 🔍 Search official docs for supported libraries:
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- [LangChain](https://python.langchain.com/docs)
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- [OpenAI](https://platform.openai.com/docs)
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- [LlamaIndex](https://docs.llamaindex.ai/en/stable)
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- 🌐 Fetch and clean documentation text directly from web pages.
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- ⚙️ Exposed as an **MCP Tool** via `server.py`.
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- ⏳ Handles timeouts gracefully to avoid breaking workflows.
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- 🔑 Configurable using `.env` with your **Serper API Key**.
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---
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## 📂 Project Structure
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```bash
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documentation/
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│── services/
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│ │──── scrape.py
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│ │──── search.py
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│
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│── tools/
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│ │──── get_docs.py
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│
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│──── config.py
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│──── server.py
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│──── pyproject.toml
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│──── .env.example
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│──── .gitignore
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```
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---
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## ⚡ Setup & Installation
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This project uses [**uv**](https://docs.astral.sh/uv/) for dependency management.
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### 1️⃣Clone the repository
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```bash
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git clone <your-repo-url>
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cd documentation
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```
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### 2️⃣ Create a virtual environment
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```bash
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uv venv
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```
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### 3️⃣ Activate the virtual environment
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Linux/macOS:
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```bash
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source .venv/bin/activate
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```
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Windows (PowerShell):
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```bash
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.venv\Scripts\Activate
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```
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### 4️⃣ Install dependencies
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```bash
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uv sync
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```
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---
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## 🔑 Environment Variables
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Copy .env.example → .env and set your Serper API key:
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```bash
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SERPER_API_KEY="your_api_key_here"
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```
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You can get an API key from [Serper.dev](https://serper.dev/)
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---
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## ▶️ Running the MCP Server
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Start the server with:
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```bash
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uv run --with mcp mcp run server.py
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```
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This runs the MCP server over stdio, making it available as a tool.
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---
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## 🛠 How It Works
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1. get_docs MCP tool
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- Input: query + library
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- Example: query="embedding models", library="openai"
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- Process:
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- Builds a site-specific query (site:platform.openai.com/docs embedding models)
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- Fetches results using Serper API
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- Scrapes and cleans text from found pages
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- Output: plain text documentation snippets
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2. Can be integrated into other MCP-compatible apps for automated documentation search & retrieval.
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---
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## 📌 Notes
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- This is Version 1 — focused on core functionality.
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- Future improvements:
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- Support for more libraries/frameworks.
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- Improved result filtering & ranking.
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- Error handling & logging improvements.
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- Possible caching layer for repeated queries.
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||||
---
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## 🐳 Docker Support
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This project also provides a Dockerfile so you can containerize the MCP Documentation Server.
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- Why Docker?
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- Consistency: run the server with the same environment on any machine.
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- Isolation: dependencies are fully contained, no need to pollute your local Python environment.
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- Portability: easy to share or deploy on servers/CI pipelines.
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- Caching: faster rebuilds thanks to Docker layer caching.
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- When do you need it?
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- If you want to run the MCP server without installing uv or Python locally.
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- If you plan to deploy on a server or inside CI/CD pipelines.
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- If you want a reproducible environment across your team.
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- How to use it?
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1. Build the image:
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```bash
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docker build -t docs-mcp:dev -f Dockerfile .
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```
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2. Run with your .env file (containing SERPER_API_KEY):
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```bash
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docker run --rm -it --env-file .env docs-mcp:dev
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```
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That’s it — the server will start inside the container and be available to any MCP-compatible client (e.g. Claude Desktop).
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||||
---
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## 💻 Running Locally with Claude Desktop
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||||
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||||
You can also connect this MCP Documentation Server directly to Claude Desktop on your machine.
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Steps:
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||||
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1. Install Claude Desktop
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- Make sure Claude Desktop is already installed on your system.
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||||
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||||
2. Enable Developer Mode
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||||
- Open Claude Desktop.
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||||
- Go to Developer Mode.
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||||
- Click Add Configuration.
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||||
This will generate a local file called:
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||||
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||||
```bash
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claude_desktop_config.json
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```
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3. Edit the Configuration File
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Open claude_desktop_config.json and add a new entry like this (adjust the paths to match your machine):
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||||
```json
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{
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"mcpServers": {
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"documentation": {
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"command": "C:\\Users\\Alawakey\\Desktop\\MCP Server\\documentation\\.venv\\Scripts\\python.exe",
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"args": ["C:\\Users\\Alawakey\\Desktop\\MCP Server\\documentation\\server.py"],
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"cwd": "C:\\Users\\Alawakey\\Desktop\\MCP Server\\documentation"
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}
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}
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}
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```
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- `command`: path to your Python executable inside the virtual environment.
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||||
- `args`: path to the `server.py` file.
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- `cwd`: working directory of your project.
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||||
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||||
4. Restart Claude Desktop
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||||
- Fully close Claude Desktop and start it again.
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||||
|
||||
5. Verify the MCP Server
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- In Claude Desktop, go to Search and Tools (near the text input).
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- You should see your MCP server listed under the name you provided (`documentation` in the example above).
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||||
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||||
6. Try some queries
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- Example prompts:
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||||
```bash
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||||
how do i implement a chroma db in langchain?
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||||
```
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||||
```bash
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||||
Use get_docs with query="ChatOpenAI" and library="openai"
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||||
```
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||||
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.
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||||
|
||||
---
|
||||
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