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Claude MCP Servers: Scalable, Secure AI Power - MCP Implementation

Claude MCP Servers: Scalable, Secure AI Power

Unleash enterprise-grade AI power with Claude MCP Serversβ€”scalable, secure, and built for your toughest workloads. Future-proof innovation, today." )

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55% of users reported increased productivity after just one week

About Claude MCP Servers

What is Claude MCP Servers: Scalable, Secure AI Power?

Claude MCP Servers are modular infrastructure components designed to extend Anthropic's Claude AI capabilities through the Model Context Protocol (MCP). These servers enable integration with external APIs, data sources, and custom workflows, providing scalable and secure interfaces for advanced AI applications. By leveraging MCP, developers can build tool-specific endpoints that enhance Claude's contextual understanding and operational efficiency.

How to Use Claude MCP Servers: Scalable, Secure AI Power?

  1. Clone the repository and install dependencies via Python package managers.
  2. Configure server parameters in pyproject.toml, including API keys and security settings.
  3. Deploy servers locally or in production environments using FastMCP runtime.
  4. Register server endpoints in claude_desktop_config.json with absolute paths.
  5. Test interactions through Claude's natural language query interface.

Claude MCP Servers Features

Key Features of Claude MCP Servers: Scalable, Secure AI Power?

  • Modular Architecture: Independent server modules for weather, media, and educational data sources
  • API Integration: Built-in support for NWS and TMDB APIs with authentication frameworks
  • Data Persistence: Structured storage for web-scraped university data
  • Security Protocols: Environment variable management for secrets and HTTPS enforcement
  • Customizability: Extensible tool definitions for adding new functionalities

Use Cases of Claude MCP Servers: Scalable, Secure AI Power?

Weather Intelligence

Provide real-time meteorological data and impact assessments through National Weather Service integration

Cultural Content Aggregation

Deliver movie/TV metadata and recommendations using TMDB's comprehensive media database

Educational Research

Access structured academic program details and institutional data from parsed university resources

Claude MCP Servers FAQ

FAQ: Common Issues & Solutions

Server not appearing in Claude Desktop

Verify JSON config syntax and ensure server paths use absolute Unix-style formatting

Authentication failures with external APIs

Confirm API keys are correctly stored in .env files and have proper scopes enabled

Performance degradation under load

Implement rate limiting middleware and consider Kubernetes orchestration for production deployments

Content

Claude MCP Servers Collection

This repository contains a collection of Model Context Protocol (MCP) servers that extend Claude's capabilities by connecting it to external data sources and APIs. These servers allow Claude to access real-time information such as weather forecasts, movie data, and university information.

πŸ“‹ Contents

  • Weather Server : Get real-time weather forecasts and alerts using the National Weather Service API
  • TMDB Movie Server : Access information about movies and TV shows via The Movie Database API
  • Amity University Crawler : Extract and query information from Amity University Bengaluru's website

πŸ› οΈ Prerequisites

Before setting up these MCP servers, make sure you have the following installed:

  • Python 3.10+
  • Git
  • uv - A fast Python package installer and resolver

πŸš€ Getting Started

Step 1: Clone the Repository

# Clone the repository
git clone https://github.com/your-username/claude-mcp-servers.git

# Navigate to the project directory
cd claude-mcp-servers

Step 2: Install uv (if you haven't already)

macOS/Linux :

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows :

curl -LsSf https://astral.sh/uv/install.py | python

After installation, restart your terminal to ensure the uv command is available.

Step 3: Set Up Each Server

Weather Server

# Navigate to the weather directory
cd weather

# Create a virtual environment and activate it
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
uv pip install -r requirements.txt

# Test the server
uv run weather.py

TMDB Movie Server

# Navigate to the tmdb directory
cd ../movieinfo/tmdb

# Create a virtual environment and activate it
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
uv pip install -r requirements.txt

# Open tmdb.py and replace YOUR_API_KEY_HERE with your TMDB API key
# You can get a free API key at https://www.themoviedb.org/settings/api

# Test the server
uv run tmdb.py

Amity University Crawler

# Navigate to the amity_crawler directory
cd ../../amity_crawler/amitycrawler

# Create a virtual environment and activate it
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
uv pip install -r requirements.txt

# Test the server
uv run amity_crawler.py

πŸ”Œ Connecting to Claude Desktop

To connect these servers to Claude Desktop, you need to configure the Claude Desktop application.

Step 1: Open the Claude Desktop Config File

The configuration file is located at:

  • macOS : ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows : %APPDATA%\Claude\claude_desktop_config.json

Create this file if it doesn't exist.

Step 2: Add Server Configurations

Add the following to your claude_desktop_config.json file:

{
    "mcpServers": {
        "weather": {
            "command": "/path/to/your/uv",
            "args": [
                "--directory",
                "/absolute/path/to/claude-mcp-servers/weather",
                "run",
                "weather.py"
            ]
        },
        "tmdb": {
            "command": "/path/to/your/uv",
            "args": [
                "--directory",
                "/absolute/path/to/claude-mcp-servers/movieinfo/tmdb",
                "run",
                "tmdb.py"
            ]
        },
        "amity": {
            "command": "/path/to/your/uv",
            "args": [
                "--directory",
                "/absolute/path/to/claude-mcp-servers/amity_crawler/amitycrawler",
                "run",
                "amity_crawler.py"
            ]
        }
    }
}

Replace /path/to/your/uv with the actual path to your uv executable. You can find this by running:

which uv  # On macOS/Linux
where uv  # On Windows

Also replace /absolute/path/to/claude-mcp-servers with the absolute path to where you cloned the repository.

Step 3: Restart Claude Desktop

After saving the configuration file, restart Claude Desktop for the changes to take effect.

πŸ” Testing the Servers

Once connected, you can test each server by asking Claude questions like:

  • Weather Server :

    • "What's the weather like in Sacramento?"
    • "Are there any weather alerts in Texas?"
  • TMDB Movie Server :

    • "What movies are currently popular?"
    • "Find me information about recent sci-fi movies."
    • "What TV shows are trending now?"
  • Amity University Server :

    • "What courses are offered at Amity University Bengaluru?"
    • "Who are the faculty members in the Computer Science department?"
    • "Tell me about the admission process at Amity University."

πŸ“š Understanding the Code Structure

Directory Structure

.
β”œβ”€β”€ LICENSE
β”œβ”€β”€ README.md
β”œβ”€β”€ amitycrawler
β”‚   β”œβ”€β”€ README.md
β”‚   β”œβ”€β”€ amity_crawler.py
β”‚   β”œβ”€β”€ main.py
β”‚   β”œβ”€β”€ pyproject.toml
β”‚   └── uv.lock
β”œβ”€β”€ movieinfo
β”‚   └── tmdb
β”‚       β”œβ”€β”€ README.md
β”‚       β”œβ”€β”€ main.py
β”‚       β”œβ”€β”€ pyproject.toml
β”‚       β”œβ”€β”€ tmdb.py
β”‚       └── uv.lock
└── weather
    β”œβ”€β”€ README.md
    β”œβ”€β”€ main.py
    β”œβ”€β”€ pyproject.toml
    β”œβ”€β”€ uv.lock
    └── weather.py

Each server follows a similar structure:

  1. Main Server File (weather.py, tmdb.py, amity_crawler.py):
* Contains tool definitions and API integration logic
* Uses FastMCP to create and run the server
  1. Data Directory (for amity_crawler):
* Stores crawled and structured data
* Persists information between server sessions
  1. Dependencies :
* Each server uses the MCP SDK
* Additional libraries like httpx for API requests and BeautifulSoup for web scraping

πŸ”§ Troubleshooting

If you encounter issues, try these solutions:

  • Server not showing up in Claude Desktop :

    • Check your claude_desktop_config.json for syntax errors
    • Ensure the paths to your servers are absolute, not relative
    • Restart Claude Desktop completely
  • Tool calls failing :

    • Check Claude's logs for errors
    • Verify your server builds and runs without errors
    • Make sure you've added any required API keys
  • Checking Claude's logs :

    • macOS: Check log files in ~/Library/Logs/Claude/
    • Windows: Check log files in %APPDATA%\Claude\Logs\

🀝 Contributing

Feel free to enhance these servers or add new ones. Some ideas:

  • Add more detailed weather information
  • Expand the movie database to include reviews and recommendations
  • Add more university information sources

πŸ“„ License

This project is licensed under the MIT License - see the LICENSE file for details.

πŸ™ Acknowledgments

  • Anthropic for creating Claude and the Model Context Protocol
  • The MCP documentation at modelcontextprotocol.io
  • The National Weather Service and TMDB for their public APIs
  • Amity University Bengaluru for educational information

Happy building with AI! πŸš€

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