Model Context Protocol
The open protocol for connecting AI applications to external tools and data sources.
What Is the Model Context Protocol
Model Context Protocol, commonly abbreviated MCP, is an open protocol originally introduced by Anthropic for connecting AI applications to external tools, data sources, and services through a single, consistent interface. Rather than requiring a developer to write a custom integration for every tool an agent might need, MCP defines a standard way for a server to expose its capabilities, such as file access, database queries, or third party APIs, and for any MCP compatible client or agent to discover and call those capabilities without knowing the implementation details underneath.
How It Works
An MCP server wraps a specific system, for example a database, a version control host, or a project management tool, and exposes its functionality as a set of tools, resources, and prompts described in a common format. An MCP client, typically the application running the AI agent, connects to one or more MCP servers, retrieves the list of what each one offers, and makes those capabilities available to the model as callable tools. When the model decides to use one of them, the client forwards the request to the corresponding server and returns the result. Because the protocol is standardized, the same MCP server can be reused by any compliant client without modification, and a client can connect to many servers built by different teams without custom glue code for each one.
Why It Matters
Before protocols like MCP, connecting an agent to a new external system typically meant writing and maintaining a bespoke integration, including its own authentication, error handling, and tool schema. MCP turns that into a one time investment: build or install an MCP server once, and it works with any client that speaks the protocol. This matters for teams operating fleets of agents, since it means new capabilities can be added by installing a server rather than rewriting agent code, and the same server can be shared across projects, teams, or organizations.
Agenhood and MCP
Agenhood agents can be extended with MCP servers, which plug in additional external tools alongside the built-in file, shell, and web tools of the Vanilla driver. This lets an operator equip an agent with access to systems like issue trackers, internal APIs, or specialized data sources without writing custom tool integrations, while keeping the underlying agent execution inside Agenhood's sandboxed container model.