MCP gateway
Definition
An MCP gateway is a single entry point that sits between your AI agents and all of your MCP servers. It holds the passwords and keys, decides which tools each agent may use, and keeps one log of every call.
By Ronan Liedmeier, fractional head of growth and course author · Published 17 October 2026 · Last updated 17 October 2026
With two or three servers you can manage each one by hand. Past that, keys end up scattered across config files and nobody can say in one place what an agent is allowed to touch.
How it differs from an MCP server
An MCP server connects an agent to one tool. A gateway sits in front of many servers and presents them to the agent as one connection. The gateway adds the right credentials to each request, checks it against its rules and forwards it, so the agent never sees the raw keys.
You set up profiles at the gateway. One profile might load analytics, the CRM and Slack for one client, while another loads a different set for a second client. Rules run at that boundary before a request reaches a tool, for example blocking delete calls or holding writes for approval.
Example
An agency produces weekly reports for six clients from the same agent setup. It puts a gateway in front of its ad platform, analytics and CRM servers and creates one profile per client. When the account manager switches to the furniture retailer's profile, the agent sees only that client's accounts and keys. On Friday the team reviews one log of every call the agent made.
Common mistake
Adding a gateway only after something goes wrong. By then keys are spread across laptops and config files and cannot be rotated in one step. A better trigger is the first time you work for more than one client, or the first time any server gets write access.
Learn it in AI Academy
- Gateways, Sprawl, and the Seven-Server RuleExpert · 6 min
Related terms
- MCP server
- An MCP server is a small program that connects an AI assistant to one tool or data source, such as Notion, Gmail or a database, using the Model Context Protocol. It tells the assistant what it can read or do there and carries out those actions when asked.
- Model Context Protocol (MCP)
- Model Context Protocol (MCP) is an open standard that lets AI apps connect to outside tools and data, such as a calendar, a CRM or a database, through one shared format. Apps that support MCP can then use those tools without a custom integration for each one.
- AI guardrails
- AI guardrails are checks and limits placed around an AI system that block or flag unsafe or wrong inputs and outputs before they cause harm. The strongest ones run as code outside the model, so a cleverly worded request can't talk its way past them.
- AI observability
- AI observability is the practice of recording what an AI system does on every run, including each model call, tool call, cost and time taken, so you can see why it behaved the way it did. It is how teams debug and monitor AI once it runs on real work.