TrueFoundry and the MCP Gateway Revolution: Insights from Gartner’s 2025 Report

The Model Context Protocol (MCP) is an open standard, launched in late 2024, that provides a universal language for AI agents to access data, APIs, and tools without custom point-to-point integrations. In practice, an AI model uses a MCP client to send a structured request, and one or more MCP servers translate that into calls to backend systems (databases, services, etc.).

As Gartner and industry vendors note, MCP is essentially “a specialized API protocol for AI agents” – securing MCP traffic is just like securing traditional APIs, but for the AI era. The adoption of MCP has exploded: Gartner observed that over 16,000 MCP servers were deployed in 2025 alone, underscoring enterprises’ growing need to connect AI assistants (copilots, chatbots, RAG systems) to corporate data and applications.

Amid this momentum, TrueFoundry was recognized in Gartner’s Innovation Insight: MCP Gateways (2025) report as one of the emerging solution providers shaping this new category. TrueFoundry’s platform stands out for bringing enterprise-grade governance, observability, and scalability to MCP adoption — addressing the exact challenges Gartner identified as barriers to secure and scalable AI integration.

However, this rise of agentic AI also creates a governance and security gap. Without a central control layer, each AI project may spin up dozens of MCP servers and credentials in an ad-hoc way. Developers manually configure and maintain each tool connection, scatter tokens across notebooks, and often bypass traditional API gateways. As one analyst put it, “autonomous AI agents calling APIs … is the missing layer in today’s infrastructure.” In practice, this means MCP servers – which could include custom or community-built connectors – proliferate without oversight, leading to “security blind spots” where agents leak data or perform unauthorized actions. Traditional tools like CNAPP or network firewalls are blind to these emerging AI-driven flows.

Enter the MCP Gateway: a new infrastructure layer positioned between AI clients (agents/models) and MCP servers, purpose-built to secure, govern, and scale AI-centric integrations. Just as API gateways became essential for web services, MCP gateways provide centralized registration of tools, enforce authentication/authorization, audit all interactions, and mediate agent requests. In Gartner’s words, MCP gateways are the “missing enterprise layer for registration, discovery, authentication/authorization, and observability across thousands of MCP servers.” They consolidate hundreds of scattered connectors into a single, managed platform — and TrueFoundry is at the forefront of making this enterprise-ready.

Key capabilities of an MCP gateway include: a central registry/catalog of approved MCP servers and tools; unified authentication (OAuth2/OpenID, SSO) and per-role access controls; policy enforcement (quota, sanitization, etc.); and full logging and observability of agent-server exchanges. All agent calls flow through the gateway, which can cache results, strip redundant context, and aggregate multi-step workflows. This prevents agents from burning through excessive tokens: for example, a gateway can intercept large responses, remove redundancy, and only forward needed parts, effectively “decreasing unnecessary token usage”. In short, an MCP gateway turns fragmented agent integrations into an organized, secure, enterprise service.

MCP Gateway Patterns (Aggregator, Proxy, Composite)

Gartner identifies three common MCP gateway deployment patterns – Aggregator, Proxy, and Composite – each with distinct trade-offs:

• Aggregator: One central gateway aggregates multiple MCP servers behind it. Agents interact only with the gateway, which fans out calls to underlying servers and then consolidates the responses. This gives developers a single endpoint to consume (“one-stop shop”) and simplifies catalog management. It also allows cross-server orchestrations (e.g. one prompt triggers calls to both CRM and ERP servers). The aggregator can enforce a unified security policy and audit all traffic. The downside is a potential single point of failure and scaling challenge: the gateway must handle all traffic to all tools. (Composio’s Universal MCP Gateway follows this model, providing 500+ managed servers behind one gateway.)
  
  
• Proxy: The gateway acts as a smart proxy in front of one or more MCP servers. In this one-to-one mapping, each MCP server still exposes its own tools, but requests are funneled through the gateway. The gateway handles cross-cutting concerns (SSL termination, authentication, logging) but does not itself combine responses. This is the simplest mode: an agent’s request to Server A goes to the gateway, which authenticates and passes it to Server A, then returns the result. It masks the actual server endpoints and allows consistent policy enforcement on every call. However, it doesn’t reduce the number of endpoints agents must call – they still “see” each tool individually – and it provides less orchestration than an aggregator.
  
  
• Composite (Hybrid/Multi-Tier): A mix of the above, often used in large or geo-distributed environments. For example, multiple regional gateways (proxies) might forward certain calls to a global aggregator, or vice versa. An organization might deploy a local edge gateway near each cloud region (for low latency), each forwarding into a central hub. This composite approach offers flexibility and fault isolation, but adds complexity. It can combine the benefits of proximity and central governance at the cost of having multiple management points. (Think of service mesh tiers – an edge gateway plus an upstream gateway.)
  
  

Each pattern can be implemented by enterprise platforms. For instance, TrueFoundry’s solution can operate in aggregator mode with a central registry, or in proxy mode by fronting specific servers on different networks. Composio’s “Universal” gateway is an extreme aggregator – it replaces hundreds of disparate servers with one managed service. Regardless of pattern, every approach requires the gateway to perform the same core functions: authentication, authorization, logging, and context management.

In all cases, the gateway should behave as a controller, not a barrier to innovation. It integrates “seamlessly with existing workflows” and supports the tools and cloud platforms developers already use. It should scale dynamically (e.g. Kubernetes auto-scaling) and be deployable across clouds, VPCs, or on-prem, ensuring low-latency access to local data.

Key Challenges and Risks with MCP Gateways

While MCP gateways unlock safer, scalable AI, Gartner notes several risks and challenges that enterprises must manage:

• Token Overuse & Cost Spikes: AI agents can inadvertently consume huge numbers of LLM tokens or API calls if workflows loop or generate verbose context. Left unchecked, a single runaway agent might incur excessive costs. Gateways help mitigate this by caching frequent queries, aggregating responses (reducing token payload), and enforcing rate limits. Without such controls, finance teams can face surprise overages as agents flood backend services.
  
  
• Integration and Compatibility Issues: MCP is still new and evolving. Not all MCP servers fully implement the spec or the same features (Anthropic’s MCP includes optional fields and transport modes). This inconsistency can lead to integration headaches. Gartner warns that “implementation consistency” is a challenge: optional features (like tool lists, formats) may not be uniformly supported, making some agent workflows brittle. Enterprises may find that one MCP server’s semantics differ from another’s, requiring adapters or testing. A robust gateway should abstract these differences: protocol translation and version compatibility are key. (For example, TrueFoundry’s gateway handles multiple MCP transport modes and server versions behind the scenes, sparing developers from these details.)
  
  
• Security Blind Spots: The very features of MCP – dynamic tool discovery and rich agent autonomy – open new attack vectors. Untrusted or poorly coded MCP servers can leak data or allow agents to traverse your network unchecked. In the wild, unsecured agents have already “quietly leaked sensitive data” and even accessed internal systems they shouldn’t. Gartner highlights the risk of “shadow” or “zombie” endpoints – unknown agent endpoints running inside the network. Without a gateway, any direct agent call bypasses typical API monitoring. Gateways must therefore enforce strong controls: prompt sanitization, egress filtering, and real-time anomaly detection. Operant AI describes this as 3D defense: continuous discovery of active tools, advanced detections of data exfiltration or malicious patterns, and active defense (blocking or quarantining untrusted servers). In short, enterprises must assume zero trust at the agent boundary.
  
  
• Governance and Compliance: Enterprises must know who called what and when, especially for regulated data (PII, customer info, IP). Every MCP transaction should be logged and auditable. Gartner’s report explicitly calls for gateways to provide a central catalog and auditing layer for MCP interactions. Without it, there’s no easy way to enforce data retention or legal hold on AI activities. TrueFoundry’s MCP Gateway, for example, logs every interaction and ties it to user/role metadata, enabling compliance with SOC2, HIPAA, GDPR, and even emerging AI standards. In contrast, a DIY setup yields fractured logs and blind spots.
  
  
• Supply Chain and Tool Hygiene: MCP servers themselves are executable code. A compromised server could inject malicious prompts or malware. Enterprise gateways must vet servers (e.g. signed or verified code) and enable pinning of approved versions. Otherwise, agents might call a rogue server posing as a trusted tool. This aspect – akin to software supply chain security – is an emerging concern. Gartner predicts the MCP ecosystem will need stronger versioning and vulnerability scanning as usage grows.
  
  

In summary, MCP gateways solve critical challenges, but they also introduce new ones. CIOs and security teams should be aware of these gaps and select solutions that address them directly – not assume legacy tools will magically cover this new infrastructure.

TrueFoundry’s Approach to MCP Gateways

TrueFoundry is one of the vendors answering this need with an enterprise-grade AI Gateway that includes a built-in MCP registry and proxy. TrueFoundry’s design emphasizes unified governance and observability of all AI tool integrations. Key features include:

• Centralised MCP Registry: Administrators define a catalog of approved MCP servers and tools in one place. Developers no longer manage dozens of disparate endpoints – they simply point to the gateway and pick from a vetted list. TrueFoundry handles OAuth2 client registration and token management centrally. Instead of each dev juggling separate API keys (often hard-coded in notebooks), the gateway provides a single platform-managed key, reducing credential sprawl.
  
  
• Federated Auth and RBAC: TrueFoundry integrates with enterprise Identity Providers (Okta, Azure AD, etc.) and supports OAuth2/OIDC end-to-end. Each AI agent or user is mapped to roles with fine-grained permissions. As the company notes, “only the right users and agents can access sensitive resources,” enforced by per-server RBAC. This transforms ad-hoc MCP calls into governed operations under SSO and corporate policy.
  
  
• Built-In Observability and Telemetry: Every MCP request and response is traced and logged. The gateway captures structured telemetry – latencies, error rates, usage metrics – and ties them to users, teams, and cost centers. Operators get end-to-end visibility of agent behavior, from IDE to cloud. Teams can filter logs by agent identity or tool, speeding troubleshooting. By centralizing logs, TrueFoundry eliminates the “fragmented visibility” of DIY setups. (TrueFoundry also partners with tracing tools like Langfuse to visualize LLM interactions, further enhancing observability.)
  
  
• Integration Ecosystem: TrueFoundry provides out-of-the-box MCP server implementations for common enterprise services – e.g., Slack, Confluence, Sentry, Datadog – so developers can call these tools via a standard interface. It also lets teams register custom or proprietary APIs as MCP servers on the portal, instantly exposing them to agents. This ability to plug in legacy and home-grown systems with minimal coding accelerates integration projects.
  
  
• Agent-Orchestration Support: Beyond simple pass-through, the TrueFoundry gateway can hold session context across calls. For example, an agent can perform a multi-step workflow (query database, then call internal API, then write a report) all tracked as a single session. The gateway helps manage that context chain, reducing token waste and agent complexity. It even offers a sandboxed “Agent Playground” where teams can prototype workflows with defined tool sets.
  
  
• Enterprise Scalability and Compliance: The platform is designed for large organizations. It is cloud- and on-premise friendly (VPC, air-gapped, multi-cloud support). It meets rigorous standards (SOC2 Type II, HIPAA, GDPR). High availability, automatic load balancing, and autoscaling ensure it can handle thousands of concurrent agent requests. TrueFoundry highlights 24/7 enterprise support and SLA commitments for critical deployments.
  
  

In essence, TrueFoundry’s MCP Gateway is a central control plane for all AI agents’ back-end access. It “consolidates security, workflow management, and observability” into a single layer. This aligns closely with Gartner’s vision: instead of each application managing its own connectors and tokens, enterprises gain one orchestration layer that enforces least privilege, audits all activity, and scales to the entire organization. By doing so, TrueFoundry claims to “enable secure, compliant AI at enterprise scale” (their words) while abstracting away the plumbing from developers.

‍

Why Enterprises Should Care – and What to Prioritize?

For technology leaders, MCP gateways are not just a niche concern – they are fast becoming a cornerstone of secure AI strategy. Any enterprise embedding AI assistants or RAG in workflows will depend on MCP. Without a robust gateway, AI projects risk exposing critical data or violating compliance. 

The Gartner report predicts that by 2026, 75% of API management vendors and 50% of iPaaS tools will offer, signaling this trend is mainstream.

When choosing an MCP gateway, leaders should prioritize:

• Comprehensive Security Controls: Look for OAuth2/OIDC integration, fine-grained RBAC, and built-in threat protection. Can the gateway sanitize prompts, enforce trust zones, and block suspicious behavior in real time?
• Scalable Architecture: The solution must handle thousands of agents and servers across multiple clouds. Features like multi-region deployment, auto-scaling, and hybrid support are crucial. It should also consolidate logs and metrics from all those agents for centralized visibility (TrueFoundry, for example, guarantees “unified observability”).
  
  
• Integration Ecosystem: Does the gateway support standard MCP transports (stdio, HTTP/SSE)? Can it proxy in custom servers or legacy APIs easily? Providers that offer pre-built connectors or low-code integrations (like SnapLogic’s pipeline-based tools) can significantly reduce time-to-value.
  
  
• Regulatory Compliance: Ensure the gateway provides detailed audit trails and meets industry compliance (SOC2, ISO 27001, HIPAA, etc.). Given emerging AI governance standards (e.g. ISO 42001 for AI), the ability to enforce data handling policies and generate compliance reports is a differentiator.
  
  
• Vendor Support and Roadmap: This is an emerging area – pick vendors investing in the MCP standard and the AI security space. TrueFoundry has been cited by Gartner as actively contributing to standards and open source. A provider’s community engagement (CNCF, OWASP involvement, etc.) can be a positive signal.
  
  

In practice, a hybrid approach often makes sense. For example, organizations can adopt TrueFoundry’s MCP Gateway as the central control plane for managing tool access, observability, and development workflows. The key is to treat agentic API calls as first-class network traffic — routing them through a secure, governed gateway rather than allowing unmanaged peer-to-peer connections.

Conclusion

AI is fundamentally reshaping how enterprises build, operate, and scale their systems but this transformation demands a new layer of infrastructure governance. MCP gateways are emerging as the backbone of AI-native architectures, much like API gateways became essential for the microservices era. By implementing a secure, scalable MCP gateway, organizations can unlock the full potential of AI agents safely and responsibly.

Enterprise leaders should begin by auditing their AI projects: where are agents making external calls, and are those connections governed? Even a single unmanaged MCP server or hardcoded API key introduces security and compliance risks. The next step is to evaluate MCP gateway platforms that align with enterprise priorities, security, observability, scalability, and developer productivity.

Now is the time to invest in structured adoption. The cost of inaction - data leaks, governance failures, or uncontrolled AI spend, will only grow as AI-driven workflows proliferate.

By making the MCP gateway a standard layer of your AI infrastructure, enterprises can secure their AI supply chain and ensure sustainable innovation. As Gartner emphasizes, this isn’t a nice-to-have , it’s the foundation for trustworthy, enterprise-scale AI. The future of AI will only be as safe, compliant, and effective as the infrastructure built to govern it.

‍