8 Best MCP Gateway Providers in 2026

Connecting an AI agent to one MCP server is relatively straightforward. Managing connections across multiple agents, applications, and teams is where things get complicated. Each MCP server can have its own authentication requirements, permissions, and configuration. As your application grows, managing these connections individually becomes harder to maintain, secure, and monitor.

An MCP gateway provides a shared layer for managing how AI agents discover and access tools. Some gateways focus on managed integrations and authentication, while others offer greater control over infrastructure, tool routing, and access policies.

This guide compares eight MCP gateway providers and projects for developers in 2026, including Composio, agentgateway, ContextForge, Docker, and several enterprise platforms. We'll look at their capabilities, deployment options, and limitations to help you choose the right one for your AI application.

What is an MCP gateway?

An MCP gateway is an intermediary between AI applications and the tools they access through the Model Context Protocol (MCP). Instead of connecting every AI agent directly to multiple MCP servers, developers can use a gateway to manage those connections through a shared interface.

For example, an AI coding assistant might need access to GitHub for repository information, Linear for issue tracking, and an internal API for deployment status. An MCP gateway can bring those tools together while managing how requests are authenticated, routed, and monitored.

Depending on the platform, an MCP gateway can:

• Aggregate tools: Make tools from multiple MCP servers available through a common endpoint.
• Manage authentication: Handle incoming client authentication and credentials for downstream services.
• Control permissions: Restrict which users, teams, or agents can access particular tools.
• Route requests: Direct tool calls to the appropriate MCP server or backend service.
• Provide observability: Record tool activity, errors, latency, and other operational information.

An MCP gateway serves a different role from an MCP server. The server exposes capabilities such as tools, resources, and prompts, while the gateway manages access to those capabilities. Gateway functionality is also distinct from MCP server hosting: a gateway can connect to servers running elsewhere without hosting them. Some platforms, such as Docker MCP Gateway, also manage the processes or containers that run those servers.

For smaller applications, direct MCP connections may be sufficient. A gateway becomes more useful when several agents or teams need consistent access to shared tools.

The 8 best MCP gateways

MCP gateways take different approaches to managing tools and connections. Some provide ready-to-use integrations, others focus on open-source infrastructure, and a few extend existing API management platforms. The list below includes both commercial providers and open-source projects.

This comparison uses vendor documentation checked on November 9, 2026, covering integration scope, deployment, authentication, governance, pricing, and limitations. It is not a hands-on performance ranking.

1. Composio

Best for: Developers who want managed MCP integrations, authentication, and centralized tool access.

Composio MCP Gateway connects AI agents to external applications and MCP servers through a shared endpoint. It combines a library of more than 1,500 managed application integrations with authentication management, tool discovery, and access controls. Developers can connect agents to applications such as GitHub, Slack, Salesforce, or Jira without building and maintaining every integration themselves.

This is particularly useful when agents need to work across several applications. Instead of configuring separate credentials and permissions for each agent, teams can manage connections through Composio.

Key capabilities

• Managed integrations: Access a large catalog of prebuilt application toolkits, with Composio handling supported authentication flows and integration maintenance.
• Centralized authentication: Manage OAuth connections, credentials, and token refresh without exposing credentials directly to agents.
• Granular permissions: Control access by organization, team, user, and action, including restricting sensitive operations within otherwise permitted tools.
• Tool discovery: Retrieve relevant tools based on an agent's task rather than loading an entire integration catalog into its context.
• Audit visibility: Track tool activity and access decisions through a shared management layer.
• Model flexibility: Keep tool connections and access policies separate from the underlying LLM provider.

Consider an engineering assistant that needs to investigate an application error. It might retrieve issues from Sentry, find related work in Linear, and read relevant repository information from GitHub. Composio gives the assistant access to those connected applications while allowing the organization to restrict which actions it can perform.

Deployment and pricing

Composio offers Hobby, Pro, and Enterprise plans. As of November 2026, Hobby is free and includes 100,000 monthly tool calls, of which up to 20,000 can use Composio-managed apps.

Pro starts at $29 per month with usage-based billing beyond included allowances. Enterprise offers custom terms and additional identity, security, and governance features, including SSO and SCIM.

Composio advertises cloud, customer-VPC, and fully self-hosted deployment options. Confirm enterprise availability, pricing, and operational responsibilities if infrastructure ownership is a requirement.

Limitations

Custom MCP is experimental and has several compatibility constraints:

• It requires a public HTTPS endpoint; local stdio servers are unsupported.
• OAuth requires Dynamic Client Registration (DCR), excluding servers that require pre-registered clients or support only Client ID Metadata Documents (CIMD).
• Private, user-specific toolkits require Pro.
• Tool changes require an explicit resync.

Composio is a good fit for teams that want to connect AI agents to their existing apps while leaving authentication and integration maintenance to the platform.

2. agentgateway

Best for: Developers who want an open-source MCP gateway with configurable routing, authentication, and access policies.

agentgateway is an open-source gateway designed to manage traffic between AI applications, MCP servers, and other backend services. It gives developers control over which servers are accessible, how requests are authenticated, and which tools individual clients are permitted to use.

It supports local MCP servers over stdio and remote servers over Streamable HTTP. Multiple MCP servers can also be combined into a virtual MCP backend, simplifying tool discovery for connected clients.

Key capabilities

• MCP federation: Combine tools from multiple servers behind a shared endpoint.
• Fine-grained authorization: Apply access policies to individual tools, prompts, and resources.
• Authentication support: Configure external authentication and gateway security policies.
• Traffic management: Apply rate limits and other policies to MCP requests.
• Observability: Use OpenTelemetry tracing, metrics, and structured logs to understand gateway activity.
• Deployment control: Run the gateway as a standalone service or within Kubernetes infrastructure.

For example, a platform team could use agentgateway to expose selected internal development tools to Claude Code and other AI applications. The gateway can filter the tools available to each client, giving the team control over access to connected servers.

Deployment and pricing

agentgateway is open source and can be deployed on infrastructure your team manages. There is no open-source license fee, but hosting, maintenance, monitoring, and operational support contribute to its total cost.

Limitations

agentgateway requires teams to configure the gateway, connect upstream servers, and manage authentication. If users must complete a separate browser-based OAuth flow for each upstream server, expose those servers on separate paths instead of multiplexing them behind one endpoint. Cross App Access using ID-JAG is an alternative only where upstream servers support it.

For teams comfortable managing infrastructure, agentgateway provides flexibility without requiring them to adopt a managed integration platform.

3. ContextForge

Best for: Platform teams that need a self-hosted gateway for managing MCP servers, tools, and APIs.

ContextForge is an open-source gateway, registry, and proxy that brings multiple MCP servers and other services behind a unified interface. Its scope extends beyond routing MCP requests: it can federate existing servers, expose REST and gRPC services as MCP-compatible tools, and maintain a centralized registry of tools, resources, and prompts.

This makes it particularly useful for organizations that already have several internal services and want to make them accessible to different AI applications without rebuilding each integration.

Key capabilities

• Tool federation: Combine capabilities from multiple MCP servers and APIs.
• Virtual MCP servers: Create curated collections of tools, prompts, and resources for different applications.
• API integration: Expose existing REST and gRPC services through MCP-compatible interfaces.
• Centralized registry: Register, organize, and manage tools and servers through a shared administrative interface.
• Authentication and access controls: Configure authentication, token scopes, and role-based access.
• Observability: Use structured logs, metrics, and OpenTelemetry tracing to monitor gateway activity.

For example, a company with separate services for customer data, billing, and internal documentation could register those services through ContextForge. Its developers could then create different virtual MCP servers for customer support agents and engineering assistants, giving each application a selected collection of capabilities.

Deployment and pricing

ContextForge is available under the Apache 2.0 license. Developers can install it through Python packages or Docker, with Kubernetes deployment options for larger environments. There is no open-source license fee, but teams are responsible for the infrastructure and services used to run it.

Limitations

ContextForge has a broader feature set than developers may need for a small MCP implementation, and running it in production involves configuring authentication, storage, networking, monitoring, and deployment infrastructure. It is most suitable for teams that specifically need MCP federation, a tool registry, or integration with existing internal APIs.

4. Docker MCP Gateway

Best for: Developers who want to run and manage MCP servers in containers.

Docker MCP Gateway is an open-source solution for managing MCP servers through Docker. Running MCP servers directly on a developer's machine can introduce dependency conflicts, inconsistent environments, and security concerns. Docker addresses these issues by running servers in isolated containers and managing their lifecycle through the gateway.

Developers can connect multiple AI clients to the gateway without configuring the same servers separately for each client. The gateway handles routing, server startup, credentials, and the connections between clients and servers.

Key capabilities

• Container isolation: Run MCP servers in containers with defined resource and network restrictions.
• Server lifecycle management: Start and manage servers as tools are requested.
• Centralized configuration: Connect multiple AI clients without repeating individual server configurations.
• Credential handling: Inject the credentials required by supported servers.
• Profiles: Group MCP servers into collections for different projects or workflows.
• Logging and tracing: Inspect tool calls and server activity.

For example, a developer working across several projects might maintain one profile containing GitHub and browser automation tools and another containing database-related tools. Those tools can be managed through Docker without installing every MCP server directly into each AI client.

Deployment and pricing

Docker MCP Gateway is open source and available through Docker's MCP tooling. Developers can use Docker Desktop with MCP Toolkit or install the gateway manually for supported environments. The open-source gateway should be distinguished from Docker's broader commercial AI Governance offering, whose MCP Gateway-related capabilities are currently invite-only.

Limitations

Docker MCP Gateway is particularly useful for containerized server management, but teams still need to manage authentication, server configurations, and infrastructure. Those looking primarily for managed SaaS integrations, centralized enterprise identity, or a fully hosted gateway may need another platform. For developers already working with Docker, it offers a familiar way to make MCP server environments more consistent and manageable.

5. Kong AI Gateway

Best for: Teams that want to manage MCP traffic alongside existing APIs.

Kong AI Gateway extends API gateway capabilities to AI applications and MCP-connected tools. For organizations that already use Kong to manage API traffic, this provides a way to expose backend services to AI agents without introducing a completely separate tool-access infrastructure.

Kong AI Gateway 2.0's AI MCP Server entities expose existing API operations as MCP tools and are managed through Konnect, its API, or kongctl. The gateway can also connect to upstream MCP services and apply supported access and traffic-management policies.

Key capabilities

• API-to-MCP integration: Expose selected backend API operations as tools for MCP-compatible clients.
• MCP proxying: Connect AI applications to existing MCP services through the gateway.
• Centralized authentication: Apply supported authentication strategies to MCP requests.
• Rate limiting: Manage request volume using gateway policies.
• Observability: Use logging and traffic monitoring capabilities already available in Kong.
• API infrastructure reuse: Manage MCP alongside existing API services.

Consider an engineering team with an internal inventory API already managed through Kong. The team could expose selected endpoints as MCP tools without building a separate MCP server for every operation. An AI agent could then retrieve inventory information while the organization continues applying gateway authentication and traffic policies.

Deployment and pricing

Kong offers different deployment and commercial options across its gateway products. The relevant requirements, licensing, and available policies depend on the Kong AI Gateway edition and version.

Limitations

Kong is particularly relevant when an organization already uses API gateway infrastructure. Teams starting from scratch may find its configuration and governance model more involved than a managed MCP integration platform. Developers should also distinguish Kong's current AI MCP Server functionality from its older AI MCP Proxy plugin, which has separate licensing and feature requirements.

Kong is worth considering when MCP is an extension of an existing API management strategy.

6. Amazon Bedrock AgentCore Gateway

Best for: Developers building AI agents with AWS services and infrastructure.

Amazon Bedrock AgentCore Gateway is a managed service that provides AI agents with a common interface for accessing external tools and backend services. It allows developers to expose supported APIs, AWS Lambda functions, and MCP servers through gateway endpoints, reducing the need to build separate tool interfaces for each application.

One of its useful features is semantic tool discovery, which helps agents find relevant tools based on their task instead of relying entirely on predefined tool names.

Key capabilities

• Managed gateway infrastructure: Use an AWS-managed service instead of operating the gateway yourself.
• API integration: Convert supported REST APIs and Lambda functions into MCP-compatible tools.
• MCP aggregation: Combine capabilities from multiple MCP targets.
• Semantic tool discovery: Help agents identify relevant tools from larger collections.
• Authentication management: Configure authentication for incoming agents and credentials used to access downstream services.
• AWS integration: Connect gateway targets to services and permissions managed within your AWS environment.

For example, a support agent might need to retrieve a customer's account status, check a billing system, and initiate an approved workflow. With AgentCore Gateway, developers can expose the relevant backend functions as tools and configure how the agent accesses them. This is especially helpful when the underlying services already run on AWS.

Deployment and pricing

AgentCore Gateway is a managed AWS service with usage-based pricing. The total cost depends on gateway usage and any additional AWS services involved, including Lambda functions, identity services, and other backend infrastructure. Review the Amazon Bedrock AgentCore pricing for current rates and billing units.

Limitations

Although AWS manages the gateway service, developers still need to configure targets, permissions, credentials, and network connectivity. The platform is also a more natural fit for teams using AWS than for organizations that want a cloud-independent gateway.

For production deployments, authentication and authorization deserve particular attention. AWS supports configurations that delegate authorization to another component, so teams must ensure that an appropriate policy engine, interceptor, or downstream service enforces access.

AgentCore Gateway is a practical option when developers want managed MCP infrastructure that fits into their existing AWS architecture.

7. Azure API Management

Best for: Developers who want to manage MCP access through existing Microsoft API and identity infrastructure.

Azure API Management provides capabilities for exposing and governing MCP servers using Azure's established API management platform. Developers can expose supported REST APIs as MCP servers or place existing remote MCP servers behind API Management. This allows teams to reuse authentication, access policies, monitoring, and deployment infrastructure they may already have in Azure.

Key capabilities

• REST API integration: Expose existing API operations as MCP tools.
• MCP server management: Provide governed access to supported remote MCP servers.
• Identity integration: Validate authentication tokens from Microsoft Entra ID or other identity providers.
• Traffic policies: Apply rate limits, quotas, IP filtering, and supported caching policies.
• Monitoring: Use Azure's existing monitoring and diagnostics capabilities.
• Infrastructure reuse: Manage MCP endpoints alongside other APIs.

For example, a company might already use Azure API Management to expose internal customer and product information. The team could expose selected API operations as MCP tools and apply its existing authentication policies, extending its current setup to support an AI assistant.

Deployment and pricing

MCP management capabilities are available across supported Azure API Management service tiers, including Developer, Basic, Standard, and Premium variants. Azure also provides a self-hosted gateway option for organizations that need to run gateway components within their own infrastructure. Costs depend on the selected service tier, deployment model, and usage.

Limitations

Microsoft's external-server documentation lists support for tools and resources, but not prompts; its overview still describes tools-only support. Verify resource support for your selected gateway and release before relying on it. MCP capabilities are also not supported within API Management workspaces.

These limitations matter if your application relies on more than tool invocation. For teams already using Microsoft Entra ID and Azure API Management, however, the platform offers a way to extend existing governance to MCP without introducing a separate gateway vendor.

8. TrueFoundry

Best for: Platform teams managing MCP tools alongside broader AI infrastructure.

TrueFoundry MCP Gateway provides a centralized layer for discovering, securing, and monitoring MCP servers used by AI applications. It is part of TrueFoundry's broader AI gateway platform, which allows organizations to manage access to models and tools through related infrastructure.

This makes it relevant for platform engineering teams responsible for supporting multiple AI applications, particularly when they need to govern both model access and tool use.

Key capabilities

• MCP server registry: Maintain a centralized inventory of internal and third-party MCP servers.
• Tool discovery: Help developers find available servers and tools.
• Identity integration: Connect supported identity providers such as Okta and Microsoft Entra ID.
• Role-based access control: Restrict access to MCP servers based on user or team permissions.
• Observability: Track tool calls, latency, errors, and usage.
• AI gateway integration: Manage MCP-related access alongside other AI infrastructure.

For example, an organization might have separate teams building customer support, engineering, and data analysis agents. TrueFoundry can provide a shared place to register available MCP servers, determine which teams can access them, and inspect usage across those applications.

Deployment and pricing

TrueFoundry offers Developer, Pro, and Enterprise plans. As of November 9, 2026:

• Developer: Free for up to three users, with a combined allowance of 10,000 requests and tool calls per user per month, five MCP servers, and one virtual MCP.
• Pro: $25 per user per month, including SSO, custom roles, and 20,000 combined requests and tool calls per user per month, pooled across the team. Additional usage costs $20 per 100,000; model-provider and managed external guardrail charges are additional.
• Enterprise: Custom terms and options such as private deployment, extended audit retention, and data residency controls.

See the TrueFoundry pricing page for current limits.

Limitations

TrueFoundry's broader AI platform may introduce more functionality than a team needs if its only requirement is connecting an agent to a few MCP servers. Some advanced governance features, private deployment options, and compliance-related capabilities also require Enterprise. It is best suited to organizations looking for centralized MCP management as part of a wider AI infrastructure strategy.

How to choose the best MCP gateway for your stack

The eight platforms above solve related problems, but they are not interchangeable. Start with the tools, infrastructure, and access requirements your application already has, then compare the platforms that fit those needs.

Choose a managed gateway if you want ready-to-use integrations

If your agents need to interact with several third-party applications, a managed platform such as Composio is a useful starting point. It provides prebuilt integrations, authentication management, and centralized tool access without requiring your team to maintain every connection.

This approach is particularly suitable for developers building AI applications that need to work with services such as GitHub, Slack, Jira, or Salesforce. Before choosing a managed gateway, check whether it supports your required applications, authentication flows, usage volume, and permissions.

Choose an open-source gateway if you want infrastructure control

agentgateway and ContextForge are worth comparing when your team needs to host and configure its own MCP infrastructure. agentgateway focuses on routing, authentication, authorization, and traffic management. ContextForge adds broader federation and registry capabilities for connecting different services.

Docker MCP Gateway is another option when running and managing containerized MCP servers is the main requirement. These platforms give developers more control over the deployment environment, but require additional operational work.

Choose an API or cloud gateway if you already have the infrastructure

For organizations already using an API management platform, extending that infrastructure to MCP may be simpler than adopting a separate gateway. The most relevant option depends on your existing environment:

• Kong: For teams already managing APIs through its gateway platform.
• Amazon Bedrock AgentCore Gateway: For developers who want managed tool access within AWS.
• Azure API Management: For organizations using Microsoft's API and identity infrastructure.

Consider how much of your existing authentication, networking, and monitoring setup each option allows you to reuse.

Choose an enterprise MCP gateway based on governance requirements

Enterprise teams often need SSO, user provisioning, centralized access policies, audit logs, private networking, and support for internal tools. Composio and TrueFoundry are worth evaluating for managed enterprise governance, while self-hosted gateways and existing cloud platforms may fit organizations with specific infrastructure requirements.

The best enterprise MCP gateway is the one that satisfies your security and operational requirements without adding unnecessary complexity. Before committing, test a real workflow and verify that:

• Unauthorized tool calls are rejected even when invoked directly.
• Credentials and sessions remain isolated between users, and revocation takes effect.
• Token audiences and consent boundaries are validated.
• Outbound destinations are restricted to protect against server-side request forgery (SSRF).
• Secrets are redacted from logs.
• Timeouts and retries cannot cause duplicate writes.

These checks provide more useful evidence than feature lists alone. The MCP security guidance provides additional detail.

Connecting an MCP gateway to live web tools

An MCP gateway manages how agents access tools. The tools themselves determine what those agents can actually do. For example, an agent researching competitors might need to search the web, retrieve information from product pages, and then update a connected project-management tool with its findings.

TinyFish's MCP integration provides web capabilities for compatible AI assistants, including searching websites, browsing pages, and extracting information. A workflow could use TinyFish to gather current information from the web, validate the extracted facts, and pass them to an application tool.

This workflow needs a clear trust boundary: treat web content as untrusted data, prevent embedded instructions from authorizing tool calls, and require approval before sensitive writes. Where supported, a gateway can manage access to the MCP connections involved, although the exact setup depends on its transport, authentication, and remote-server requirements.

When designing the workflow, distinguish the gateway's connectivity and governance role from the capabilities supplied by the connected MCP servers.

Choose an MCP gateway that fits your architecture

Start with the tools your agents need, the permissions they should have, and the infrastructure your team can realistically maintain. Composio is a useful starting point for developers who want managed integrations, authentication, and centralized access to application tools.

agentgateway, ContextForge, and Docker provide different levels of infrastructure control, while Kong, AWS, Azure, and TrueFoundry fit broader platform requirements.

If managed integrations and authentication are your priorities, explore Composio MCP Gateway as a starting point.