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Agents no longer just talk to a model. They act. An agent reads a database, opens a ticket, sends a message, and moves a record, and it does all of this through the Model Context Protocol (MCP), the standard that connects an AI client to the tools it can use. As MCP moves from demos into production, every one of those tool calls becomes a security decision and an audit event: which agent called which tool, with what arguments, on whose behalf, and what came back. Left unmanaged, an agent wired directly to a dozen MCP servers is a dozen ungoverned paths into the systems that run the business.
The MCP gateway is the layer that closes that gap. It sits between AI clients and the MCP servers they reach, federates many servers behind one governed endpoint, decides who is allowed to call what, and records every invocation. This guide compares the six MCP gateways most relevant to enterprise AI security in 2026, starting with NeuralTrust TrustGate and then covering five other platforms, so security and platform leaders can see where each one fits and, just as important, where each one stops.
TL;DR
- An MCP gateway is the control layer between your AI clients and the MCP servers they call. It centralizes tool discovery, identity, access control, and audit logging so agents reach tools through one governed door instead of many open ones.
- A catalog of connectable MCP servers is now table stakes. Every gateway in this guide ships one; each lets teams connect servers and expose a curated set of tools from a single endpoint. Catalog breadth is a convenience, not a security property.
- NeuralTrust TrustGate is the only MCP gateway in this list with runtime security built in. Its Security Engine inspects the content of tool calls inline and reasons across a whole session, rather than only deciding who is allowed to call a tool.
- The other five lead on access governance and connectivity. Arcade centers on tool authorization, Composio and Merge on managed connector breadth, Runlayer on approved-access workflows, and TrueFoundry on a broader Kubernetes-native platform.
- The dimension buyers most often underweight is runtime content security on tool calls. Identity, RBAC, and audit logs decide whether a call is allowed; they do not inspect what is inside the call or the response for injection, data leakage, or abuse spread across turns.
- The right choice follows from your primary problem: built-in runtime security, the broadest managed connector catalog, identity-scoped employee access, or a wider AI platform.
What Is an MCP Gateway?
An MCP gateway is a specialized control layer that sits between AI clients and agents on one side and the MCP servers they consume on the other. Where an agent would otherwise connect to each MCP server directly, one wire at a time, the gateway becomes the single door all of that tool traffic passes through, so control can attach in one place instead of being reimplemented per agent.
In practice, an MCP gateway does four things. It federates MCP servers into one endpoint and a catalog: teams register built-in, third-party, or custom MCP servers, and the gateway exposes a curated collection of tools that a client connects to with a single URL, often in a click. This catalog is common to the whole category; every gateway in this comparison has one, and catalog size mostly reflects how many managed connectors a vendor maintains. It governs access: identity-aware policy, role-based access control, and tool scoping decide which user, team, or agent may see and call which server and which specific tools. It produces an audit trail: every tool call is logged, so who called what, with what arguments, and with what outcome is recoverable for compliance and investigation. And, in the dimension that matters most for security teams, it gives enforcement somewhere to attach: a single point where policy is applied to tool traffic.
That last dimension is where the six gateways differ most. Most govern access, deciding whether a given caller is allowed to reach a given tool, and most add audit logging on top. Far fewer inspect the content of the tool call and its response at runtime, looking for prompt injection carried through a tool result, sensitive data leaving in an argument, or an abuse pattern that unfolds across several innocuous-looking calls in the same session. Access control answers "is this caller allowed?"; runtime security answers "is what is actually flowing through this call safe?" Understanding that split is the key to choosing well, and it is the lens this guide uses throughout.
Comparison at a Glance
| Gateway | Category | MCP server catalog | Access governance | Runtime security | Deployment | Best for |
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| NeuralTrust TrustGate | Security-first MCP control plane | Integrated catalog of 200+ third-party MCP servers, connectable in one click | Identity, per-consumer tool access, and policy on every route | Built-in Security Engine inspects tool-call content inline, with session memory across the whole conversation | SaaS, hybrid, and fully self-hosted / air-gapped | Security-led teams that want tool governance and runtime enforcement in one gateway |
| Arcade | MCP runtime and tool-authorization gateway | Federates tools from multiple MCP servers into one gateway endpoint; large integration library | Per-gateway tool selection, OAuth-based end-user auth, and OIDC user sources | Focused on secure authorization of tool calls; no documented content-inspection engine | Arcade Cloud, cloud marketplaces, and self-host via Helm / hybrid | Developers who need per-user tool authorization for agents |
| Composio | Enterprise MCP gateway for connectivity | 1,000+ managed integrations with per-team scoped endpoints | SSO, SCIM, per-team whitelisting/blacklisting, and blocked destructive actions | Metadata-only audit logging and access control; no documented runtime content-inspection engine | Managed cloud platform | Orgs rolling managed tools out to many teams quickly |
| Merge Agent Handler | Governance layer for employee AI access | Thousands of maintained connectors plus public MCP servers | IdP provisioning, role/team-scoped access, and DLP scanning before data reaches third parties | DLP scanning on tool calls; no documented runtime detection engine for injection or multi-turn abuse | Managed cloud platform | IT and security teams governing how employees connect AI tools |
| Runlayer | MCP gateway for approved, governed access | Built-in connectors plus registered and custom MCP servers, with a request-and-approve catalog | Identity- and context-aware policy scoped to user, group, role, agent, client, tool, and resource | Real-time security scans and audit logging on each call; positioned around approved access rather than a first-party detection engine | Managed platform (SOC 2, HIPAA, GDPR) | Teams that want approved MCP access to be the default for employees |
| TrueFoundry | MCP gateway inside a broad AI platform | Central registry of internal and third-party MCP servers, prebuilt and custom | RBAC per server, OAuth 2.0, and federated identity (Okta, Azure AD) | Observability, tracing, and audit on tool calls; security via access control rather than a content-inspection engine | Kubernetes-native; VPC, on-prem, air-gapped, multi-cloud | Kubernetes-native enterprises consolidating models, agents, and tools |
The 6 Best MCP Gateways in 2026
1. NeuralTrust TrustGate
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Every gateway in this list can connect an agent to MCP servers and decide who is allowed to call what. TrustGate is the only one built by a security company, and that shows up in the one thing none of the others document: security enforcement built into the gateway itself.
TrustGate ships an integrated catalog of more than 200 third-party MCP servers, the tools teams use every day, connectable in one click and governed through the gateway from the moment they are enabled. Because every connected server runs through the gateway, it is under policy and observation by default. Identity, per-consumer tool access, and policy attach to every route, so which user, team, or agent may reach which server, and which specific tools inside it, is decided centrally rather than per agent.
The difference is what happens inside the call. A Security Engine attaches to every route and inspects each request and response inline, enforcing an allow, block, or transform decision before the call reaches its target. Crucially, it maintains session memory: it reasons about a whole conversation rather than one call at a time, so it can catch an attack or abuse pattern deliberately spread across several innocuous-looking tool calls. Access control decides whether a caller is allowed; TrustGate also decides whether what is flowing through the call is safe.
Deployment runs wherever the data has to live: SaaS for speed, hybrid for regulated environments, and fully self-hosted or air-gapped for defence, government, and financial sector. Every tool call is logged and traced, and events export natively to the SIEMs a security team already runs.
- Catalog: integrated catalog of 200+ third-party MCP servers, connectable in one click
- Access governance: identity, per-consumer tool access, and policy on every route
- Runtime security: built-in Security Engine inspects tool-call content inline, with session memory across the whole conversation
- Deployment: SaaS, hybrid, fully self-hosted / air-gapped; native SIEM export
- Best for: security-led teams that want tool governance and runtime enforcement in one place
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2. Arcade
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Arcade positions itself as an MCP runtime that federates the tools from multiple MCP servers into a single gateway endpoint, so an agent, application, or IDE connects once and reaches a curated collection of tools. Teams choose exactly which tools each gateway exposes, mix and match tools from different servers, and connect any MCP client that supports the Streamable HTTP transport through a single gateway URL.
Its center of gravity is authorization. Arcade groups gateway users into modes, from Arcade Auth for internal use to a production mode that redirects end users to an OIDC identity provider and identifies them by a subject claim, plus a header-based fallback for clients that cannot run a browser OAuth flow. It maintains a large integration library and supports self-hosting via Helm alongside its cloud and marketplace options. Its documented strength is secure, per-user authorization of tool calls rather than inline inspection of tool-call content.
- Catalog: federates tools from multiple MCP servers into one endpoint; large integration library
- Access governance: per-gateway tool selection, OAuth end-user auth, OIDC user sources
- Runtime security: focused on tool authorization; no documented content-inspection engine
- Best for: developers who need per-user tool authorization for agents
Related article: NeuralTrust vs Arcade: MCP Gateway Comparison 2026
3. Composio
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Composio presents its MCP gateway as the way to roll tools out to every team in an organization, backed by more than 1,000 managed integrations. Each team gets a unique MCP endpoint that developers paste into their AI client; SSO authenticates the user, and only the tools that team is allowed to use appear.
Its governance model is team-scoped and identity-driven. Admins whitelist or blacklist toolkits per team, block destructive actions within otherwise-allowed toolkits, and let members request access to blocked tools for approval. SSO via SAML and OIDC plus SCIM provisioning map directory groups to teams, so new hires get the right tools and offboarded users lose access. Every tool call is logged as metadata (user, team, tool, action, outcome) with no payloads stored and configurable retention, and Composio is SOC 2 Type II and ISO 27001 certified. Its focus is managed connectivity and access governance at scale rather than inline content inspection of each call.
- Catalog: 1,000+ managed integrations with per-team scoped endpoints
- Access governance: SSO, SCIM, per-team whitelist/blacklist, blocked destructive actions
- Runtime security: metadata-only audit and access control; no documented content-inspection engine
- Best for: organizations rolling managed tools out to many teams quickly
Related article: NeuralTrust vs Composio: MCP Gateway Comparison 2026
4. Merge Agent Handler
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Merge Agent Handler (in its Agent Handler for Employees configuration) is positioned as a governance layer for how employees connect AI tools such as Claude, ChatGPT, and Cursor to the systems IT manages. Employees connect their preferred MCP-compatible client with a single URL, and every action runs under their identity with policy applied automatically.
The model is built around identity and data control. It connects to Okta, Azure AD, or any SCIM-compatible provider, provisions users, and assigns tool access based on existing roles and groups. IT defines which tools each team or user can access, and every tool call is scanned for sensitive data before it reaches a third-party system, with DLP rules that can block, redact, or mask flagged content. Merge maintains thousands of connectors and supports importing public MCP servers, and every tool call is logged with the employee's identity, the action, and the outcome. Its runtime protection centers on DLP-style data scanning rather than a documented detection engine for prompt injection or abuse that unfolds across a session.
- Catalog: thousands of maintained connectors plus public MCP servers
- Access governance: IdP provisioning, role/team-scoped access, DLP scanning before data leaves
- Runtime security: DLP scanning on tool calls; no documented injection or multi-turn detection engine
- Best for: IT and security teams governing how employees connect AI tools
Related article: NeuralTrust vs Merge Agent Handler: MCP Gateway Comparison 2026
5. Runlayer
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Runlayer frames its MCP gateway around a single goal: making approved MCP access the default. Employees use their preferred AI clients, and platform and security teams get control over what happens between those clients and MCP servers. Teams publish approved capabilities once, starting from built-in connectors, registering existing MCP servers, or deploying custom MCP servers onto managed infrastructure, and employees discover and request access from a catalog.
Its access model is notably granular. Policy scopes access by user, group, role, agent account, client, connector, tool, resource, OAuth state, network, and runtime condition before a call runs, and it supports 300+ AI clients. Each call passes OAuth and session validation, identity-aware policy, real-time security scans, analytics, and audit logging, and platform teams use that runtime activity to refine access over time. Runlayer is SOC 2 certified and HIPAA and GDPR compliant. Its design leans toward approved-access workflows and per-call scanning rather than a first-party runtime detection engine reasoning across an entire session.
- Catalog: built-in connectors plus registered and custom MCP servers, with a request-and-approve catalog
- Access governance: identity- and context-aware policy across user, group, role, agent, client, tool, and resource
- Runtime security: real-time security scans and audit logging per call; approved-access oriented
- Best for: teams that want approved MCP access to be the default for employees
Related article: NeuralTrust vs Runlayer: MCP Gateway Comparison 2026
6. TrueFoundry
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TrueFoundry offers its MCP gateway as one component of a broad, Kubernetes-native enterprise AI platform that also spans model serving, an AI gateway, an agent gateway, and prompt management. The MCP gateway gives unified, discoverable access to MCP servers across cloud, on-prem, and hybrid, managed from a central registry.
Its strengths are registry, discovery, and enterprise deployment. Teams register internal or third-party MCP servers, auto-discover the available tools with the right access control, and connect enterprise tools through prebuilt or custom MCP servers. Access is governed with RBAC per server, OAuth 2.0 with dynamic discovery, and federated identity through Okta, Azure AD, or other IdPs, and every request is traced and audited for observability. Deployment is a clear differentiator: VPC, on-prem, air-gapped, or multi-cloud, so data stays within the customer's infrastructure, backed by SOC 2, HIPAA, and GDPR. Security is delivered through access control, observability, and audit rather than a dedicated content-inspection engine on tool traffic.
- Catalog: central registry of internal and third-party MCP servers, prebuilt and custom
- Access governance: RBAC per server, OAuth 2.0, federated identity
- Runtime security: observability, tracing, and audit; security via access control
- Deployment: Kubernetes-native; VPC, on-prem, air-gapped, multi-cloud
- Best for: Kubernetes-native enterprises consolidating models, agents, and tools
Related article: NeuralTrust vs TrueFoundry: MCP Gateway Comparison 2026
How to Choose the Right MCP Gateway
Most buyers approach this decision by breadth: "which one has the most connectors?" That is a reasonable first filter, but it selects for convenience, not safety. Every gateway here has a catalog; catalog size mostly tells you how many managed integrations a vendor maintains, not how well the traffic through them is secured.
The sharper question is: once an agent can reach a tool, what actually governs the call? There are two layers, and they are not the same.
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Access governance decides whether a caller is allowed to reach a tool: identity, RBAC, tool scoping, approval workflows, and audit logging. Every gateway in this list does this, and several do it very well. Composio and Merge tie it tightly to your IdP and org structure; Runlayer scopes it down to individual tools and runtime conditions; TrueFoundry backs it with RBAC and federated identity across a broader platform; Arcade centers it on per-user OAuth authorization.
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Runtime security decides whether what is flowing through the allowed call is safe: inspecting the content of tool calls and responses for injection, data leakage, and abuse, including patterns that only appear across several turns of the same session. This is where the list narrows to one. NeuralTrust TrustGate is the only MCP gateway here with a built-in Security Engine that inspects tool-call content inline and reasons across a whole session. DLP scanning (Merge) and per-call security scans (Runlayer) sit partway toward this, but they are not a first-party detection engine with session memory.
So the decision comes down to your primary problem. If you need the widest managed connector catalog to roll tools out fast, Composio and Merge are built for that. If you need per-user tool authorization for developer-built agents, Arcade fits. If you want approved access to be the organizational default, Runlayer is designed around it. If you are consolidating models, agents, and tools on Kubernetes, TrueFoundry belongs in that evaluation. And if the tool call itself has to be secured, not just permitted, TrustGate is the only option in this list that enforces at that layer.
The one dimension every team underweights: runtime content security on tool calls. Identity and audit tell you who did what; they do not stop a poisoned tool result from steering an agent, or sensitive data from leaving in an argument. If your roadmap puts agents in front of real systems, check specifically whether a gateway inspects the call, or only authorizes it.
See how TrustGate compares to your current MCP gateway
Key Takeaways
- An MCP gateway is the control layer between AI clients and the MCP servers they call, federating servers into one governed endpoint and centralizing tool discovery, identity, access control, and audit logging.
- A catalog of connectable MCP servers is standard across the category. All six gateways have one, so catalog breadth is a convenience factor, not a security differentiator.
- The real divide is between access governance (is this caller allowed?) and runtime security (is what is flowing through the call safe?). Every gateway does the former; almost none do the latter.
- NeuralTrust TrustGate is the only MCP gateway in this guide with runtime security built in: a Security Engine that inspects tool-call content inline and reasons across a whole session, plus flexible deployment up to air-gapped and native SIEM export.
- Arcade, Composio, Merge, Runlayer, and TrueFoundry are strong on connectivity and access governance, each tuned to a different buyer, from developer authorization to employee IT governance to a broad Kubernetes-native platform.
- The right choice follows from your primary problem: built-in runtime security, the broadest managed catalog, identity-scoped employee access, approved-access-by-default, or a wider AI platform.
Frequently Asked Questions
1. What is an MCP gateway?
An MCP gateway is a control layer that sits between AI clients or agents and the MCP servers they call. It federates many MCP servers behind one governed endpoint and catalog, decides which user, team, or agent may reach which server and tools, and logs every tool call for audit. It is the checkpoint for agent tool traffic, the place where identity, access control, and, in some gateways, runtime security enforcement attach in one place rather than being rebuilt per agent.
2. How is an MCP gateway different from an AI (LLM) gateway?
An AI gateway governs traffic between applications and language-model providers: routing, failover, token-based cost control, and prompt/response handling. An MCP gateway governs traffic between AI clients and the tools agents call over the Model Context Protocol: tool discovery, identity-scoped access, and per-tool-call audit. They solve adjacent problems, and some platforms offer both, but an MCP gateway is specifically the control point for the tool calls that let an agent act, not just reason.
3. Doesn't every MCP gateway have a catalog of MCP servers?
Yes. A catalog of connectable MCP servers is common to the whole category, and every gateway in this guide ships one, letting teams connect servers and expose a curated set of tools from a single endpoint, often in a click. Catalog size mostly reflects how many managed connectors a vendor maintains. Because it is standard, catalog breadth is a convenience factor rather than a security property, and it should not be the only basis for a decision.
4. What is the difference between access control and runtime security in an MCP gateway?
Access control decides whether a caller is allowed to reach a given server or tool, using identity, RBAC, tool scoping, and approval workflows. Runtime security decides whether what is actually flowing through the call is safe, inspecting the content of tool calls and responses for prompt injection, data leakage, or abuse, including patterns that only emerge across several turns. Most MCP gateways do access control well; far fewer inspect the content of the call itself at runtime.
5. Which MCP gateway is best for enterprise AI security?
For a security-first mandate, NeuralTrust TrustGate is purpose-built around a Security Engine that inspects tool-call content inline and reasons across a whole session, not just per call, on top of tool governance and audit. The other gateways can be secured well through identity, RBAC, DLP scanning, and audit, but that is access governance, a different model from a dedicated runtime detection engine. The best fit depends on whether you want enforcement on the content of the call or only on who is allowed to make it.
6. Why does runtime security on tool calls matter if I already have RBAC and audit logs?
RBAC decides who may call a tool, and audit logs record what happened after the fact. Neither inspects the live content of a call. A tool result can carry a prompt injection that steers the agent; an argument can carry sensitive data out to a third party; an abuse pattern can be split across several calls that each look harmless. Runtime content inspection is what catches these in the moment, before the call completes, which access control and logging alone cannot do.
7. Can an MCP gateway connect to custom or internal MCP servers?
Yes. Every gateway in this guide supports registering internal or custom MCP servers alongside built-in and third-party connectors. Approaches vary: some let you deploy custom servers onto managed infrastructure, some register any internal API or endpoint into the catalog, and some import public MCP servers. Once registered, those servers are governed through the gateway like any other, which is a large part of the point, since it brings internal tools under the same identity, access, and audit controls.
8. How do MCP gateways handle identity and access?
Most MCP gateways tie into an enterprise identity provider such as Okta, Azure AD, or a generic OIDC/SCIM setup, then scope access by user, team, role, or agent. From there they typically allow tool scoping (which specific tools inside a server a caller can see and use), and several add approval workflows so users can request access to blocked tools. This ensures each tool call runs under a known identity and that access follows the org chart, so new hires and offboarded users are handled automatically.
9. Do MCP gateways support self-hosted or air-gapped deployment?
Some do and some do not. Several gateways in this guide are managed cloud platforms, while others support VPC, on-premises, air-gapped, and Kubernetes-native deployment for teams that require data to stay inside their own infrastructure. NeuralTrust TrustGate and TrueFoundry both document deployment up to air-gapped environments. If you operate in defence, government, healthcare, or financial services, deployment model and data residency are often decisive, so confirm them early.
10. Should an MCP gateway replace my API gateway or my AI gateway?
Usually not. An MCP gateway governs a specific kind of traffic, the tool calls agents make over the Model Context Protocol, and it is normally layered alongside existing infrastructure rather than replacing it. General service traffic stays on your API gateway, and model traffic stays on your AI gateway if you run one. The MCP gateway adds a governed control point for agent tool use, which is a distinct problem with distinct requirements, so most enterprises run it as an addition, not a substitute.
About the Author
Alessandro Pignati is Lead AI Security Researcher at NeuralTrust, where he leads research on AI and agentic security, advancing techniques to evaluate and secure large language models and autonomous AI systems. He specializes in adversarial machine learning, AI red teaming, LLM security, and AI safety, contributing to the development of secure and trustworthy AI.
NeuralTrust is an AI agent security platform, recognized in the Gartner 2025 Market Guide for AI Gateways and Guardian Agents, and the KuppingerCole 2025 Leadership Compass for Generative AI Defense. Headquartered in Barcelona with ISO 27001 certification.