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Glossary

What is MCP Gateway?

A centralized control plane for Model Context Protocol traffic. An MCP gateway secures, manages and routes connections between AI agents and MCP servers, enforcing zero-trust access controls and providing visibility into every tool invocation.

In depth

An MCP gateway is a managed access layer between AI agents and the Model Context Protocol servers that expose tools, data and capabilities. It terminates the agent's connection, authenticates the user behind the agent, evaluates fine-grained authorization policies on every tool call, and forwards permitted requests to the appropriate MCP server. In return, it produces a complete audit trail of every tool invocation and the data that flowed back. Without a gateway, organizations either run MCP servers as raw stdio processes on developer machines (impossible to govern) or expose them directly over the network (no central policy or audit).

Why it matters

MCP is rapidly becoming the standard way AI agents interact with enterprise systems. Treating each MCP server as an unmanaged endpoint reproduces the credential-sprawl and shadow-IT problems that plagued the last decade of SaaS. A gateway gives security teams the same control point for AI tool access that they have for human user access: one identity boundary, one policy engine, one audit log.

Common use cases

  • Centralizing access to Box, GitHub, Slack and Salesforce MCP servers behind one URL
  • Enforcing per-user, per-team or per-agent authorization on tool calls
  • Replacing local stdio MCP servers with managed remote endpoints
  • Producing tamper-evident audit logs for compliance teams
  • Detecting and blocking toxic-flow patterns across chained tool calls

How Ferentin handles it

Ferentin is the trust layer for AI agents. The platform centralizes identity, policy enforcement and audit across LLMs, MCP servers and AI tools. MCP Gateway is one of the primitives this trust layer is designed around. See the platform overview for how it fits into the service edge, control plane and observability plane.

Related terms

MCP (Model Context Protocol)

An open protocol that standardizes how AI models connect to external tools, data sources and services. MCP defines a client-server architecture where AI applications (clients) communicate with tool providers (servers) through a structured interface, enabling AI agents to take actions in the real world.

AI Gateway

A centralized proxy layer that sits between users or applications and AI services (LLMs, MCP servers, AI tools). An AI gateway enforces security policies, manages authentication, routes requests across providers and logs all interactions for audit and observability.

Toxic Flow

A threat pattern unique to AI agents where the combination of tools and data in an agent's context becomes dangerous, even when each tool is safe on its own. Toxic flows arise when an agent chains actions together, such as reading sensitive data from one source and sending it to an external service. They enable prompt injection, data exfiltration and tool misuse. Detecting toxic flows requires context-aware policy enforcement that inspects not just the current request but the full sequence of tools invoked and the data in scope.

Policy Enforcement

The runtime evaluation and application of security rules to AI requests. Policy enforcement can include checking user identity, validating permissions, applying token or rate limits, filtering content and blocking unauthorized actions, all before a request reaches the AI service.

Trustworthy Agents

AI agent systems that meet five key properties: keeping humans in control, aligning with human values, securing agent interactions, maintaining transparency and protecting privacy. Trustworthiness cannot be achieved through model safety alone. It requires coordinated infrastructure across the model, the harness (instructions and guardrails), the tools (external services) and the environment (runtime context). A well-trained model can still be exploited through an overly permissive tool or an exposed environment. Enterprise trust layers enforce these properties through identity-based access control, policy enforcement, audit trails and context-aware security at the tool and environment layers.

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