// Module included in the following assemblies:
//
// * ai_applications/mcp_server/mcp-server-overview.adoc

:_mod-docs-content-type: CONCEPT
[id="ai-app-mcp-server-mcp-gateway-overview_{context}"]
= MCP gateway overview

[role="_abstract"]
The primary goal of a Model Context Protocol (MCP) gateway is to standardize and simplify how AI models interact with external data and tools. Without a gateway, an AI model would need a custom integration for every single database, API, or local file system it needs to access.

== Aggregation and orchestration
A gateway connects to multiple MCP servers simultaneously. Instead of the LLM (Large Language Model) managing ten different connections, it communicates only with the gateway. The gateway then routes the LLM's request to the specific server that holds the relevant information (for example, fetching Jira tickets from one server and Google Drive docs from another).

== Security and access control
The gateway acts as a security perimeter. 

It manages:

* *Authentication*: Holding the API keys or tokens required for various servers.

* *Permissions*: Ensuring the AI model only accesses the specific data or tools that the user authorized.

* *Privacy*: Stripping sensitive information before it is sent back to the model provider.

== Protocol translation
The gateway ensures that the communication between the AI model and the servers follows the MCP standard. It translates the high-level "intent" of the AI model into the specific JSON-RPC calls that the MCP servers understand.

== Scalability
By decoupling the AI model from the data sources, you can add, remove, or update MCP servers without ever needing to change the core configuration of your AI application.

