MCP (Model Context Protocol) Server
Allows listing repository commits and authenticates using GitHub Personal Access Token.
Enables place search functionality with configurable search radius using the Google Maps API.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP (Model Context Protocol) Servercalculate the distance between New York and Los Angeles"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP (Model Context Protocol) Integration Examples
This repository demonstrates the usage of Model Context Protocol (MCP) with various integrations including a custom calculator server, GitHub, and Google Maps.
What is MCP?
Model Context Protocol (MCP) is a protocol that enables AI models to interact with external tools and services. It provides a standardized way for AI models to:
Execute tools and functions
Access resources
Generate prompts
Interact with external services
Related MCP server: MCP Server Demo
Project Structure
├── server.py # MCP server (custom creation)
├── client_server.py # MCP Client
├── 1)maps.py # Google Maps MCP integration
├── 2)github.py # GitHub MCP integration
└── requirements.txt # Project dependencies
Features
1. Custom Calculator Server
Implements basic arithmetic operations
Demonstrates MCP tool creation
Shows resource and prompt handling
2. GitHub Integration
List repository commits
Uses GitHub Personal Access Token for authentication
Demonstrates environment variable handling
3. Google Maps Integration
Search for places using Google Maps API
Configurable search radius
Environment variable based API key management
Setup
Install dependencies:
pip install -r requirements.txt
MCP Tools Types
Tools: Functions that perform specific actions
Defined using
@mcp.tooldecoratorCan accept parameters and return values
Resources: Static or dynamic data sources
Defined using
@mcp.resourcedecoratorAccessed using resource URLs
Prompts: Template-based text generation
Defined using
@mcp.promptdecoratorCan include dynamic content
Best Practices
Always use environment variables for sensitive data
Implement proper error handling
Use type hints for better code clarity
Document your tools with clear docstrings
Keep API keys secure and never commit them to version control
Usage Examples
Calculator
Server side ( initialize )
@mcp.tool(name="add")
def add(a: int, b: int):
return a + b
Running server.py file, it will up and run the MCP server.
Client side ( utilize )
result = await session.call_tool("add", arguments={"a": 5, "b": 3})
Running client_server.py file, it will connect to the MCP server.
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceA modular server that implements the Model Context Protocol standard, providing tools for interacting with GitHub, GitLab, Google Maps, Memory storage, and web automation through a unified gateway.2473Apache 2.0
- FlicenseNot gradedqualityDmaintenanceA demonstration implementation of the Model Context Protocol server that facilitates communication between AI models and external tools while maintaining context awareness.
- FlicenseNot gradedqualityDmaintenanceA Flask-based server that implements Model Context Protocol to enhance LLMs with external tool capabilities via natural language, allowing tools like weather lookup and calculations to be invoked directly in the model's text output.2
- AlicenseCqualityFmaintenanceA customizable Model Context Protocol server implementation that enables AI models to interact with external tools including weather queries, Google search, and camera control functionality.114Apache 2.0
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