Model Context Protocol (MCP)
What is MCP and why it matters for AI applications
Overview
The Model Context Protocol (MCP) is an open standard that enables AI assistants to securely connect with external data sources and tools. Developed by Anthropic, MCP provides a universal way for AI models to interact with your systems while maintaining security and control.
Why MCP Matters
Traditional AI assistants are limited to their training data and built-in capabilities. MCP changes this by allowing AI to:
- Access Real-Time Data: Connect to databases, APIs, and live systems
- Use External Tools: Execute code, manage files, or interact with third-party services
- Maintain Context: Share relevant information across conversations and sessions
- Preserve Security: Control exactly what the AI can access and do
How It Works
MCP defines a standard protocol for communication between three components:
- MCP Client: The AI assistant (like Claude) that wants to use tools
- MCP Server: A service that provides specific tools or data access
- Transport Layer: The communication method (HTTP, stdio, etc.)
Key Concepts
Tools
Functions that the AI can call, like “search_database” or “send_email”. Each tool has:
- A name and description
- Input parameters
- Expected output format
Resources
Data sources the AI can read from, like files or API endpoints.
Prompts
Pre-configured templates that help the AI use tools effectively.
Security Model
MCP includes built-in security features:
- Authentication: Verify the AI’s identity before granting access
- Authorization: Control which tools each AI can use
- Audit Logging: Track all AI-tool interactions
- Rate Limiting: Prevent abuse or excessive usage
In Civic Labs
We’re extending MCP with additional security and management capabilities:
- MCP Hub: Centralized management and discovery of MCP servers
- Guardrail Proxy: Add security policies without modifying servers
- Bodyguard: Detect malicious prompts before they reach tools
- Pass-through Proxy: Insert custom logic into the MCP flow