The Model Context Protocol (MCP) is an emerging standard designed to enable AI models — particularly AI agents — to securely connect to external tools, data sources, and applications in a consistent, interoperable way. MCP specifies how an AI system can discover available capabilities, request context, and invoke actions, while allowing the host environment to enforce security and governance controls.

Purpose:

MCP was created to address the growing complexity of multi-agent and agent-to-app interactions by providing a common, standardized “handshake” between AI models and the services they use. This improves interoperability, security, and observability compared to ad-hoc integrations.

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Who maintains it:

The Model Context Protocol (MCP) was originally developed by Anthropic and is now maintained as an open specification with public documentation and reference implementations. The project is community-driven, with contributions from independent developers, enterprise users, and ecosystem partners. Governance is designed to encourage interoperability across vendors while preserving strong security and compliance guardrails.

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Key Characteristics:

• Standardized Communication — Defines how AI models request and receive context or capabilities from connected tools.
• Security-Aware Design — Allows granular permissioning and policy enforcement over what agents can access or do.
• Multi-Agent Coordination — Facilitates collaboration between different AI agents or models using shared context.
• Extensibility — Supports a wide variety of data formats, APIs, and use cases through a consistent protocol layer.

Use Cases:

• Securely connecting AI agents to SaaS applications or cloud services.
• Enabling multiple AI systems to coordinate tasks in complex workflows.
• Managing context retrieval for large-scale AI deployments in enterprise environments.

Security Considerations:

MCP implementations should be paired with least-privilege access, activity monitoring, and audit logging to prevent misuse, data leakage, or unauthorized actions by connected agents.

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Example in Practice:

A customer service AI agent using MCP could connect to a company’s CRM and ticketing system through governed integrations, fetch relevant customer data, and update records — all without hardcoding API calls or bypassing security controls.‍

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Related terms:

• MCP Server – The system or service that hosts tools or data sources accessible through MCP.
• MCP Client – A software component that connects to MCP servers to securely discover, request, and use tools or data following the Model Context Protocol.
• AI Agent Protocol – A standardized method for AI agents to securely communicate, negotiate, and exchange data with other agents, tools, or services.