Technology
Model Context Protocol (MCP)
MCP is the open standard that enables AI agents to connect with external tools, databases, and services. It's become "the natural language for AI integration"—as essential to AI agents as HTTP is to web applications.
What is MCP?
Model Context Protocol (MCP) is an open standard and open-source framework introduced by Anthropic in November 2024 to standardize how AI systems like large language models integrate and share data with external tools, systems, and data sources.
MCP provides a universal interface for reading files, executing functions, and handling contextual prompts. Think of it as a USB port for AI—a standardized way to plug in any tool or data source without custom integration work.
“Running an MCP server has become almost as popular as running a web server.”
— Industry observation, 2025
What MCP Enables
1Tool Integration
AI agents can interact with databases, APIs, file systems, and external services through a standardized protocol. No more custom integrations for each tool.
2Context Sharing
MCP allows AI systems to share context across applications. An agent can pull data from one system and use it to inform actions in another.
3Agent Collaboration
Multiple AI agents can coordinate through MCP, sharing knowledge and working together on complex tasks that span multiple systems.
4Prompt Handling
MCP standardizes how prompts and context are passed to AI models, enabling consistent behavior across different implementations.
Industry Adoption
MCP has seen rapid adoption across the AI ecosystem:
- ●March 2025: OpenAI officially adopted MCP across their products, including ChatGPT desktop
- ●Google DeepMind integrated MCP into their agent frameworks
- ●LangChain, Hugging Face, Deepset added MCP support to their developer tools
- ●December 2025: Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation
There are now tens of thousands of MCP servers available for different tasks, tools, and integrations, curated on directories like MCP.so.
MCP and Context Graphs
MCP and context graphs are complementary technologies:
| MCP | Context Graphs |
|---|---|
| Connects agents to tools | Stores decision traces |
| Enables actions | Provides context for decisions |
| Standardizes integration | Captures institutional memory |
| The "plumbing" | The "knowledge" |
MCP gives agents the ability to do things. Context graphs give agents the knowledge to decide what to do. Together, they enable truly autonomous AI agents that can both act and reason.
How MCP Works
MCP uses a client-server architecture:
┌─────────────────┐ MCP Protocol ┌─────────────────┐
│ │ ◄──────────────────► │ │
│ AI Agent │ │ MCP Server │
│ (MCP Client) │ │ (Tool/Service) │
│ │ │ │
└─────────────────┘ └─────────────────┘
│ │
│ • Send requests │ • Expose tools
│ • Receive responses │ • Handle queries
│ • Handle context │ • Return data
│ │- ●MCP Clients are AI applications that need to access external resources
- ●MCP Servers expose tools, data sources, or services in a standard format
- ●The Protocol defines how they communicate—requests, responses, context
Frequently Asked Questions
What is Model Context Protocol (MCP)?
MCP is an open standard introduced by Anthropic that standardizes how AI systems integrate with external tools, data sources, and services. It provides a universal interface for AI agents to interact with the outside world.
Who created MCP?
MCP was introduced by Anthropic in November 2024. In December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation, with support from OpenAI, Block, and other companies.
How does MCP relate to context graphs?
MCP provides the connectivity layer (how agents connect to tools), while context graphs provide the knowledge layer (what decisions were made and why). MCP enables actions; context graphs inform decisions.