I developed a custom Model Context Protocol (MCP) server that enables AI assistants like Claude to securely access external data sources and tools. This implementation follows Anthropic's open-source MCP specification, creating a bridge between large language models and real-world data and functionalities.

The MCP server follows a client-server architecture with three core components:

MCP Host: The AI application (Claude) that initiates connections and manages interactions

MCP Client: Protocol client that maintains a 1:1 connection with the server

MCP Server: My implementation that exposes specific functionalities through the MCP protocol

The server supports two transport models:

STDIO (Standard Input/Output): Used for local integrations, communicating through standard I/O streams

SSE (Server-Sent Events): Implemented for remote integrations using HTTP requests

Both transport models use JSON-RPC 2.0 as the message format for data transmission.

My MCP server exposes a set of custom tools that allow Claude to:

Query databases for real-time information

Generate reports based on data analysis

Automate web-based tasks through browser automation

The server provides consistent access to read-only data sources, similar to file paths or database queries, while maintaining strict security boundaries.

Isolated credential management to protect sensitive data

User authorization for all interactions

Prevention of unauthorized access to external resources

Requirement Analysis: Identified specific data sources and tools needed

Protocol Implementation: Used the MCP SDK to implement server-side protocol operations

Tool Development: Created custom tools with clear input/output specifications

Testing & Validation: Verified functionality with Claude and other MCP-compatible hosts

Security Audit: Ensured all connections maintain proper isolation and authorization

Backend: Python for server implementation

Protocol: MCP Python SDK integration

Transport: Implemented both stdio and HTTP-based SSE support

Data Sources: Connected to PostgreSQL database and REST APIs

This MCP server implementation enables AI assistants to:

Access real-time data without compromising security

Execute complex operations through well-defined tool interfaces

Maintain clear system boundaries while expanding AI capabilities

The implementation follows best practices for MCP servers, including proper error handling, capability discovery, and efficient message exchange patterns.

This project demonstrates my ability to work with cutting-edge AI integration technologies while maintaining strong security practices and clean architecture.