Glossary · AI & Agents
Model Context Protocol (MCP)
Overview
What MCP does
MCP standardizes how AI applications talk to the outside world. Instead of building a separate custom integration for every model and every tool, developers expose their data and functions through an MCP server, and any MCP-compatible AI client can use them. Think of it as a universal adapter—often compared to USB-C—for connecting language models to context.
An MCP server can offer three things: tools the model can call to take actions, resources it can read for context, and prompts it can reuse. The client, such as an AI assistant, discovers these capabilities automatically.
Why MCP matters
Before MCP, connecting a model to a CRM, database, or file store meant bespoke code that broke whenever an API or model changed. MCP replaces that many-to-many integration problem with one shared protocol, so a tool built once works across many AI clients, and swapping the underlying model doesn't require rewriting integrations.
For a business, that means faster delivery, less lock-in, and reusable connectors. It has been adopted across major AI platforms, making it a practical foundation for agent and automation projects rather than a single vendor's proprietary system.
How we use MCP
We use MCP when building AI agents and workflow automation so the models can safely reach the systems your business runs on—CRMs, databases, ticketing, and internal APIs. Standardized connectors mean each integration is built once, tested, and reused, which keeps projects maintainable as models and tools evolve.
Where we use it
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How our team puts Model Context Protocol to work in real projects.
FAQ
Model Context Protocol — common questions
Who created MCP?
Anthropic introduced the Model Context Protocol as an open standard in late 2024. It is openly specified and has since been adopted by other AI vendors and a growing ecosystem of tool providers.
Is MCP the same as function calling?
No. Function calling is how a single model decides to invoke a tool; MCP is the standard way to expose those tools, data, and prompts so any compatible client can discover and use them. They work together.
Do I need MCP for my AI project?
You don't strictly need it, but MCP saves significant effort when an AI system must connect to several tools or when you want to avoid rebuilding integrations each time you change models. For simple single-tool cases, direct integration may be enough.
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