Centralizing AI Tool Access with the MCP Gateway (original) (raw)

We’ll walk through the evolution of AI tool integration, explain what the Model Context Protocol (MCP) is, and show why MCP alone isn’t production-ready. Then we’ll explore real-world gateway implementations for connecting AI agents to external tools.

OpenAI-compatible and lightweight MCP Gateways

Designed to make MCP tools easily accessible to agents and AI clients. Prioritize compatibility with OpenAI APIs, local dev setups, or UI tools.

*Any system mimicking OpenAI’s API format

MCP Bridge

MCP Bridge is a lightweight gateway that connects MCP tools with clients using the OpenAI API format. Its purpose is to make MCP tools accessible to any LLM client, including those without native MCP support, by providing a compatible interface. If you are already using OpenAI clients, this is the lowest-friction path to tool-augmented AI without changing your existing stack.

Key features:

• OpenAI-compatible endpoints: AI clients can use OpenAI-style chat/completions APIs to interact with MCP tools without modification.
• MCP tool compatibility: Supports any tool registered with an MCP server, including tools that use structured prompts or sampling workflows, via standard JSON-RPC calls.
• SSE bridge for external clients: Exposes a Server-Sent Events interface, allowing web-based UIs or agent frameworks to receive live updates from MCP tools without polling or maintaining a WebSocket connection.

Built to expose new tools to MCP by wrapping REST APIs or CLI tools. Often support no-code or low-code setup for faster development.

Lasso

Source: Lasso1

Unlike lightweight gateways such as MCP Bridge or productized platforms like Zapier MCP Gateway, Lasso is built specifically for enterprise-grade security.

Launched in April 2025, Lasso MCP Gateway is an open-source proxy and orchestration layer that sits between AI agents and multiple MCP servers.

Think of it as a central coordination point, rather than having each agent connect individually to every tool; all communication flows through the gateway. This simplifies the architecture and makes it easier to manage tool access, routing, and performance.

Key features of Lasso

• Plugin-based security enforcement: A customizable guardrail system allows developers to enforce security at the request/response level. Plugins like Presidio (for PII detection) can inspect, sanitize, or block traffic before it reaches tool endpoints.
• Structured audit logging: All tool calls, prompt executions, and resource reads are logged in JSON format, with integrations for ELK, Prometheus, and Grafana.
• Multi-protocol support: HTTP, WebSocket, SSE, and STDIO, making it adaptable across deployment environments.
• Centralized governance: Virtual servers and tool registration enable admins to define context-specific endpoints for teams or use cases, with per-user and per-tenant access policies.

Enterprise-grade MCP Gateways

Focused on large-scale deployment, security, tool routing, session management, and policy enforcement across teams.

Zapier MCP Gateway

Source: Zapier2

In early 2025, Zapier launched its MCP interface. Zapier’s MCP layer converts its 8,000+ app integrations into MCP-compatible endpoints, enabling LLMs and agent frameworks to invoke real actions (e.g., sending Slack messages, updating Salesforce records, triggering Gmail automations) with minimal setup.

Zapier MCP Gateway offers a library of 1000+ app connections and 30,000+ actions via MCP-compatible endpoints. It’s free to use with rate limits of 80 calls/hour, 160/day, and 300/month.

It provides built-in authentication, rate limiting, and endpoint management.

Representative workflows:

• Query and update a PostgreSQL database using natural language
• Send scheduled announcements to community platforms
• Create and manage calendar events from a single user request
• Extract and summarize web content, then forward to Slack
• Handle end-to-end complex tasks, booking a flight and adding it to a calendar through a single chat prompt

AI workflows you can build with Zapier MCP Gateways

• Work with databases: Enable your AI to locate, insert, or modify data within a PostgreSQL table, based on natural language input.
• Engage communities: Have your AI send a scheduled message.
• Manage scheduling: Automatically create calendar events by a single user request.
• Summarize online content: Ask the AI to extract key information from a webpage using tools like Web Parser, then forward the summary to Slack or another messaging app.
• Enable fully conversational assistants: Let users plan complex tasks like booking flights through chat.
  For example, a user can say, “Book a window seat on the next flight to Chicago and add it to my calendar,” and the AI can search for flights, make the reservation, schedule the event, and confirm.

IBM ContextForge

IBM ContextForge is an Apache 2.0-licensed open-source gateway that federates MCP servers, agent-to-agent (A2A) protocols, and REST/gRPC APIs behind a single endpoint. With 3,500+ GitHub stars and active development, it is the most feature-complete open-source option in the current market. A2A protocol support makes it forward-compatible with multi-agent architectures coming in MCP’s Q3 2026 roadmap.3

Kong AI Gateway

Kong AI Gateway is suited for enterprises already running Kong for API management. Its key differentiator is the ability to expose existing REST APIs as MCP tools without writing MCP server code, a significant advantage for organizations with large existing API surface areas.4

MintMCP Gateway

MintMCP holds SOC 2 Type II certification a critical differentiator for healthcare and financial services deployments, where procurement teams would otherwise require months of security review before approving MCP adoption. It includes comprehensive audit trails, RBAC, and real-time monitoring.5

MCP Gateway demo

Watch how the AI Gateway uses MCP to track and manage agentic traffic in action. In this demo, three AI agents interact with GitHub, Linear, and OpenAI.

Source: Lunar Dev6

Why does MCP need a Gateway?

In November 2024, Anthropic introduced the Model Context Protocol (MCP), an open standard designed to give AI models a consistent way to interact with external tools and data sources.7 Before MCP, every time an AI system needed to interact with an external tool, developers built a custom integration from scratch: learning each system’s API, writing connector code, handling authentication, managing rate limits, and maintaining everything as both systems evolved.

MCP eliminated that per-integration tax. By March 2026, MCP had reached 97 million monthly SDK downloads and over 5,800 community-built servers, with every major AI provider Anthropic, OpenAI, Google DeepMind, Microsoft, and AWS having adopted it. 8

MCP defines what agents communicate and how they communicate with tools. What it doesn’t define is the where, when, and under what conditions the operational and security layer that production deployments require.

The fragmentation problem that emerged

As teams built more integrations, a predictable set of problems surfaced: duplicated logic, inconsistent interfaces, no standardized audit trails, authentication tied to static secrets, and no defined gateway behavior. Token handling, session management, rate limiting, and security policies were being solved ad hoc in each integration rather than centrally. Maintenance became burdensome, and security risks compounded at scale.

Source: Bluetick Consultants9

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What an MCP Gateway Does

An MCP Gateway is a reverse proxy purpose-built for the Model Context Protocol. It sits between AI agents and MCP servers, routing all tool communication through a single, governed entry point rather than allowing each agent to connect directly to each tool.

Traditional API gateways handle stateless HTTP request/response pairs. MCP Gateways must handle persistent sessions, tool discovery handshakes, streaming responses via SSE or WebSocket, and context that accumulates across multiple tool calls within a single agent turn. This is why bolting MCP support onto a standard API gateway does not work well; the protocol needs infrastructure that understands its semantics.

Core capabilities an MCP Gateway provides:

• Centralized routing and registration: All agent requests flow through a single endpoint. The gateway identifies the requesting agent, determines which tool is being invoked, and routes to the appropriate MCP server.
• Policy enforcement and governance: Consistent access control (who can use what), rate limiting (how often tools can be called), and tenant isolation (keeping data and sessions separate across users and teams).
• Session continuity: Maintains shared memory and scoped session data, allowing agents to retain context across multi-step interactions.
• Operational efficiency: Instead of each agent opening separate connections to every tool with redundant handshakes, authentication flows, and serialization, the gateway handles routing, batching, and session reuse centrally.
• Unified observability: Logging, monitoring, error tracking, and usage analytics handled in one place. The EU AI Act’s high-risk system requirements, taking full effect in August 2026, require comprehensive logging and traceability for every AI system interaction, including tool calls. A gateway is the practical mechanism for meeting this requirement.10

MCP Roadmap: What’s Coming for Gateways

The official MCP 2026 roadmap, published in March 2026 by lead maintainer David Soria Parra, identifies enterprise readiness as one of four top-priority areas, alongside transport scalability, agent-to-agent communication, and governance maturation.11

The specific enterprise gaps the roadmap targets:

• Enterprise-managed auth (Q2 2026): Moving away from static client secrets toward OAuth 2.1 with PKCE for browser-based agents and SAML/OIDC integration for enterprise identity providers (Okta, Azure AD). This unblocks deployments in regulated industries.
• Gateway and proxy patterns: Well-defined protocol behavior when a client routes through an intermediary, covering authorization propagation, session semantics, and what the gateway is permitted to inspect. These are currently undefined, creating architectural uncertainty.
• Configuration portability: A standard for configuring an MCP server once and having that configuration work across different clients.
• Agent-to-agent coordination (Q3 2026): Enabling one agent to call another through MCP, creating hierarchical architectures where orchestrator agents delegate to specialized sub-agents.
• MCP Registry (Q4 2026): A curated, verified server directory with security audits, usage statistics, and SLA commitments.12

The roadmap is explicit that these are priorities, not commitments, and that an Enterprise Working Group has not yet formed. Organizations running MCP in production today should build for these gaps now: implement structured audit logging, avoid hard dependencies on static secrets, and architect for gateway intermediaries even before the protocol standardizes them.13

Real-world MCP gateway applications

1. MCP Bridge as a lightweight gateway

Here you can see how to configure and run a lightweight MCP gateway, specifically MCP Bridge, which serves as a compatibility layer between OpenAI-style clients and MCP tools.

Source: GitHub14

Key configuration sections in MCP Bridge:

This setup enables clients like Open Web UI to communicate with MCP tools without needing native MCP support.

When Open Web UI is configured to point to http://localhost:9090/v1 (the MCP Bridge server), and a user enters a prompt such as:

“Extract data from site and summarize”

Open Web UI converts that prompt into a structured API call, which MCP Bridge then routes to the appropriate MCP tool:

The structured API call:

2. WebSocket transport in MCP Bridge

This example shows how MCP Bridge, functioning as an MCP Gateway, supports WebSocket-based transport between AI clients and the MCP SDK. It enables real-time, bi-directional communication using the JSON-RPC format.

We’re sending and receiving structured messages, specifically MCP SessionMessage objects, which encapsulate JSON-RPC messages like: { “jsonrpc”: “2.0”, “method”: “tools/list” }

Here is what happens inside the loop:

1. Client sends a JSON-RPC message over WebSocket to list available tools:

2. MCP Bridge receives the message, decodes it, and wraps it into a SessionMessage: The standard format expected by the MCP SDK.

3. The reader loop continuously listens for incoming messages from the client, while the writer loop streams responses back to the client.

4. These responses are formatted and sent using WebSocket. This enables the client to interact with the tool registry in real time.

Source: AI Engineer15

Is an MCP Gateway the same as an API Gateway?

Not exactly. API gateways are built for traditional client-server communication. MCP Gateways, on the other hand, are optimized for AI agents, supporting context-aware workflows, session management, and standardized tool orchestration via the Model Context Protocol.

MCP Gateway vs. API Gateway

Cite this research

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Cem Dilmegani and Sena Sezer (2026) - "Centralizing AI Tool Access with the MCP Gateway". Published online at AIMultiple.com. Retrieved April 2, 2026, from: https://aimultiple.com/mcp-gateway [Online Resource]

Dilmegani, C., & Sezer, S. (2026, April 2). Centralizing AI Tool Access with the MCP Gateway. AIMultiple. https://aimultiple.com/mcp-gateway

@misc{dilmegani2026, author = {Dilmegani, Cem and Sezer, Sena}, title = {{Centralizing AI Tool Access with the MCP Gateway}}, year = {2026}, month = apr, howpublished = {\url{https://aimultiple.com/mcp-gateway}}, note = {AIMultiple. Retrieved April 2, 2026} }

Cem Dilmegani

Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Researched by

Sena Sezer

Industry Analyst

Sena is an industry analyst in AIMultiple. She completed her Bachelor's from Bogazici University.

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