MCP (Model Context Protocol) is an open standard that allows AI applications to connect to external tools, data sources, and workflows in a consistent and reusable way.
Core idea
- Without MCP, each AI application usually needs a custom integration for every external tool or data source.
- With MCP, both the AI application and the external system can follow the same standard protocol, making integrations more standardized, reusable, and easier to build.
Why MCP matters
MCP helps AI systems:
- access external data
- use tools
- perform actions
- retrieve up-to-date context beyond their training data
Background
MCP was introduced by Anthropic in 2024 as an open standard for connecting AI models to external tools and data sources.
When building AI assistants such as Claude, developers often needed to connect models to many different systems, such as:
- files
- databases
- APIs
- developer tools
- knowledge bases
Before MCP, these integrations usually required custom code for each connection.
Anthropic proposed MCP as a universal protocol to make these connections more consistent, reusable, and easier to scale.
Example
Imagine you ask an AI assistant:
“Help me check a GitHub repository, read the latest issues, and summarize what changed recently.”
Without MCP:
- the AI app may need a separate custom integration for GitHub
- if you later want it to access files, databases, or another tool, each one may need its own separate integration
- this makes development more fragmented and harder to scale
With MCP:
- the AI application can connect to external systems through a standard protocol
- GitHub, files, databases, or other tools can expose their capabilities in a consistent way
- the AI can then use the same general connection model to access different tools and data sources
In simple terms, MCP makes it easier for AI systems to connect to many external tools without reinventing the integration each time.