What's in the MCP stack?

The Model Context Protocol (MCP) is quickly became the "USB-C for AI applications," providing a standard way to connect AI models with external data sources and tools.

Since Anthropic introduced MCP in November 2024, it has grown from a technical specification into a thriving ecosystem with significant commercial opportunities. This growth is similar to the API tooling market that emerged around companies like Postman and Kong.

We're witnessing history repeat itself, but accelerated. Just as the API economy spawned billion-dollar infrastructure companies in the 2010s, MCP is creating similar opportunities today, only faster and with AI at the center. The parallels are striking: where Postman democratized API testing and Kong built the gateway layer, we're now seeing MCP-native companies emerge across hosting, infra, development tooling, authentication, and marketplace discovery.

The early indicators are compelling. Over 1,000 community-built MCP servers have emerged within six months of launch, while enterprise adoption is being driven by companies like OpenAI, Stripe, Intercom, Notion, Replit, and Sourcegraph.

The MCP ecosystem: 11 key categories

Rather than cataloging individual MCP servers (which every SaaS will eventually have, like APIs today), this market map focuses on the infrastructure and tooling companies that enable MCP adoption. These are the "picks and shovels" of the MCP ecosystem - the platforms, tools, and services that make it easy for any company to create, deploy, secure, and manage production-ready MCP servers.

1. MCP server generation & dev tools

From OpenAPI specs to runnable MCP servers

Development tools and SDKs form the backbone of the MCP development ecosystem, making it easier for developers to adopt, prototype, and deploy MCP-compatible servers and clients across languages and environments.

OpenAPI & doc-based generators

• Speakeasy: Convert OpenAPI → TypeScript with built-in runnability
• Stainless: Free OpenAPI → MCP generation with Cloudflare deployment
• Gravitee: API management bridge
• Zuplo: Expose your API endpoints as MCP-compatible tools, resources, and prompts
• OpenAPI MCP Generator: CLI scaffolding tool for MCP-compatible projects
• Open-MCP: Turn web APIs into MCP servers in 10 seconds with open registry
• ScaleMCP: Turn APIs into AI agents
• Tadata: Launch a hosted MCP server
• Liblab: SDK automation
• Tyk Technologies: Native protocol unification
• Spring AI: Java-native MCP tooling
• RapidMCP: Zero-config API wrapping

2. MCP native frameworks

These frameworks provide the "Rails" or "Django" for the agentic era. Ensures cross-model compatibility (the "Hit Rate") so a tool works as well for a small local model as it does for GPT-4o.

• FastMCP: Python leader
• Mastra: Orchestration-native
• Prismatic: Embedded B2B workflows
• Swell: Node-based logic builder
• Agno: Stateful multi-agent fleets
• LastMile AI: Cross-server tool orchestration

3. MCP hosting & infrastructure

Deploy and scale MCP servers without DevOps complexity

Platform and infrastructure providers offer foundational services that support MCP server deployment, hosting, and management, enabling enterprises and developers to effectively integrate MCP.

Developer Runtimes (PaaS)

• Alpic.ai: Vercel-like developer experience‍
• Railway: Simple MCP server deployment with auto-scaling‍
• Cloudflare Workers: Edge deployment with OAuth capabilities
• ‍Fly.io: Distributed app runtime
• ‍Supabase Edge Functions: Edge compute with Postgres integration
• ‍Northflank: Cloud PaaS
• ‍Modal: Ephemeral Python compute
• ‍Deno Deploy: Edge JavaScript runtime‍
• Render: Alternative to Railway, supports background workers (good for async MCP workflows)

Managed Tool Hubs (SaaS)

• Supermachine.ai: Marketplace-to-cloud
• ‍Smithery: Containerized registry
• ‍ScaleMCP: Managed MCP deployment
• ‍Toolhouse: Hosted tool infrastructure

4. Auth & identity for MCP

OAuth for MCP

• Scalekit: Modular auth platform for AI applications. Drop-in OAuth 2.1 specifically designed for MCP servers
• Descope: Add compliant, OAuth-based authorization to MCP servers and deploy them
• Stytch: Connected apps built for AI agents, MCP servers
• Auth0: Identity platform with auth for GenAI
• Golf.dev: Secret management

5. Data privacy & guardrails

Treat every tool response as a potential leak. This is the "DLP" (Data Loss Prevention) layer for AI.

• Lasso Security: Dedicated MCP proxy
• Nightfall AI: Real-time DLP
• Guardrails AI: Output validation
• Pangea: Security APIs for agents
• Lakera: Prompt injection defense

6. AI & LLM gateways

Consolidating model and tool traffic to optimize for cost and speed.

• Portkey: Cross-model tool routing
• Lunar.dev (MCPX): Agent service mesh
• Helicone: Observability and caching
• Martian: Model routing layer
• LiteLLM: Open-source model proxy
• Traefik: Cloud-native gateway

7. Enterprise MCP gateways

These tools govern the internal inventory of tools.

• Obot.ai: Enterprise tool catalog
• MintMCP: Compliance-first gateway
• TrueFoundry: Governance-heavy AI platform
• Kong: AI gateway plugins

8. Multi-MCP composition & orchestration

• MetaMCP: Composition GUI
• McGravity: Dependency management
• CrewAI: Multi-agent orchestration
• LangChain: Agent workflow framework
• Pipedream: Workflow automation

9. Internal enablement & registries

Marketplaces and discovery platforms help users easily find, evaluate, and adopt MCP tools and servers, enhancing visibility and community engagement.

MCP Marketplaces

• Glama: Public and private MCP directory‍
• mcp.so: Community-indexed hub ‍
• Smithery: Registry with CLI install commands
• ‍PulseMCP: GitHub-linked community index

10. MCP-specific testing & QA

Accelerate development with specialized debugging and testing tools. Streamline iterative processes for developers through IDE integrations and inspectors.

• MCP Jam: Developer workbench
• TestSprite: Autonomous QA
• Promptfoo: Evaluation framework
• SoapUI: API testing suite
• Testomat.io: Test management platform

11. Agentic observability (Telemetry)

Tracing the "Reasoning Path." Not just "did the API work," but "did the model choose the right tool?"

MCP proxies, gateways, & middleware

• Traceloop: OpenTelemetry for agents
• HoneyHive: Hit-rate metrics
• Arize Phoenix: LLM observability
• LangSmith: Agent debugging platform
• Langfuse: Open-source LLM observability
• AgentOps: Agent monitoring and analytics

Market analogies and growth potential

The MCP infrastructure market is expanding in critical areas essential to widespread adoption and effectiveness:

• Developer experience: Companies in this space deliver tools that significantly reduce the complexity developers face when moving from concept to production. The emphasis is on intuitive design, rapid prototyping, and eliminating cumbersome setup tasks.
• OAuth 2.1 compliance: Security is foundational. Successful providers integrate robust security measures, specifically OAuth 2.1 compliance, directly into their solutions, ensuring safety and reliability from day one.
• Ecosystem integration: Effective MCP infrastructure fits naturally into existing developer workflows. Companies thriving here provide integrations and compatibility with commonly used developer environments, ensuring minimal disruption and friction.
• Scalability: Successful MCP solutions reliably handle extensive workloads and scale easily from startups to enterprise-grade deployments without complex re-engineering or significant downtime.
• Standards compliance: As MCP specifications evolve, companies that remain agile, quickly adopting and supporting new standards, are best positioned for longevity and leadership.

Emerging market opportunities providing significant growth potential include:

• No-code MCP creation: Platforms that allow users without technical expertise to build functional MCP servers visually or intuitively, democratizing access and adoption.
• AI-optimized server generation: Advanced tools specifically designed to create MCP servers optimized for AI-agent interactions, going beyond basic API-to-MCP conversions to deliver more sophisticated integrations.
• Cross-platform deployment: Solutions that offer universal compatibility across multiple cloud providers, enabling flexibility and reducing vendor lock-in risks.
• Analytics platforms: Specialized tools that offer detailed visibility into AI-agent interactions through targeted analytics, empowering teams to understand, optimize, and refine deployments based on performance data.
• Compliance automation: Automated tools that simplify ensuring MCP servers comply with evolving regulatory requirements, reducing the complexity and cost of manual compliance processes.

Final word

MCP is shaping up to be a foundational layer for the next generation of AI-native applications. As the ecosystem grows, we're seeing new standards, tools, and business models emerge that echo the early days of the API economy.

The MCP space represents a classic "picks and shovels" opportunity. As MCP solidifies as the AI integration standard, the market will expand broadly in the following categories:

• Horizontal platforms addressing core infrastructure challenges (authentication, hosting, testing)
• Developer-first tooling accelerating time-to-market
• Enterprise-focused solutions emphasizing security, governance, and compliance
• Integration specialists linking MCP to established enterprise systems

Whether you're building AI infrastructure, experimenting with agentic systems, or just looking to plug in safely and scalably, the MCP landscape offers a ton of opportunity.

Want to plug into the growing MCP ecosystem without reinventing auth? Sign up for a Free Forever account with Scalekit and get your MCP servers secured and ready to scale. Have questions about architecture or integrations? Book time with our auth experts.

FAQs

What is the Model Context Protocol in AI architecture?

The Model Context Protocol or MCP acts as a universal standard for connecting AI models to external data sources and tools. Often described as the USB C for AI applications it streamlines how agents interact with various systems. By providing a consistent framework MCP allows developers to build interoperable servers and clients that function across different environments. This standardization reduces the engineering overhead required to integrate disparate SaaS applications enabling faster deployment cycles for complex AI driven workflows while maintaining a structured approach to data exchange between models and enterprise infrastructure.

Why is OAuth 2.1 critical for securing MCP servers?

Security is foundational for enterprise AI adoption and OAuth 2.1 provides the robust framework necessary for protecting MCP servers. As AI agents move beyond simple queries to performing actions on behalf of users implementing standardized authorization ensures that every interaction is verified and scoped correctly. Using OAuth 2.1 compliance prevents unauthorized access and mitigates risks associated with data leakage in AI native applications. For developers leveraging a platform like Scalekit to handle this layer means they can focus on core functionality while ensuring their MCP integrations meet modern CISO requirements for secure agent to machine communication.

How does MCP improve agent to agent authentication workflows?

MCP facilitates agent to agent communication by establishing a predictable protocol for tool calling and resource sharing. In multi agent systems authenticating these automated interactions requires specialized machine to machine or M2M identity management to ensure each agent has the appropriate permissions. By utilizing MCP in conjunction with modular auth providers engineering teams can implement Dynamic Client Registration or DCR to manage agent identities at scale. This setup ensures that when one AI agent invokes an MCP server the transaction is secure and traceable. This architectural approach is essential for scaling complex autonomous workflows across different departments or external partner ecosystems safely.

What are the primary infrastructure categories in MCP?

The MCP ecosystem is organized into nine key categories that support the full lifecycle of AI integration. These include server generation tools managed hosting platforms and specialized auth layers for user management. Additionally the market features MCP as a Service providers testing and debugging tools and registries for discovering prebuilt servers. Connection proxies and client interfaces further round out the stack allowing developers to route and manage multi MCP deployments efficiently. Understanding these categories helps technical architects choose the right picks and shovels to build scalable secure and maintainable AI applications without reinventing basic infrastructure components.

Can MCP servers handle enterprise grade SaaS integrations effectively?

Yes MCP servers are specifically designed to bridge the gap between AI models and enterprise SaaS tools like Stripe or Slack. By exposing API endpoints as standardized MCP tools and resources companies can create a unified interface for their AI agents to interact with business data. Using MCP as a Service platforms further simplifies this by providing preauthenticated connectors that reduce integration friction. This approach supports Cloud Identity Management and Discovery or CIMD principles allowing enterprises to leverage their existing software investments within new AI workflows while ensuring agents have real time access to the specific data needed to perform high value tasks.

What role does Scalekit play in MCP development?

Scalekit serves as a modular authentication platform specifically tailored for the needs of AI applications and MCP servers. It provides a drop in OAuth 2.1 implementation that allows developers to secure their AI tools without building complex identity infrastructure from scratch. By handling the intricacies of user management and agent authorization Scalekit ensures that MCP deployments are production ready and compliant with enterprise security standards. This allows engineering teams to accelerate their time to market while maintaining a high level of security and scalability for their agentic systems and B2B integrations.

How do developers debug and test MCP servers?

Debugging MCP servers involves using specialized tools like the MCP Inspector which provides a visual interface for monitoring request and response cycles. Developers can also utilize IDE integrations within VS Code or Cursor to manage and test their MCP endpoints directly within their coding environment. These tools allow for iterative testing ensuring that tool definitions and resource prompts are correctly interpreted by AI models. By leveraging these testing frameworks teams can identify and resolve configuration issues early in the development process leading to more reliable and predictable AI agent performance in production environments.

Why is MCP compared to the API economy?

The growth of MCP mirrors the evolution of the API economy where companies like Postman and Kong built essential infrastructure for web services. Just as APIs became the standard for software communication MCP is emerging as the standard for AI integration. This shift is creating a massive market for developer tooling hosting and security services tailored to the unique requirements of LLMs. The acceleration is even faster this time with thousands of community built servers appearing shortly after launch. For CTOs this signals a major architectural shift towards modular AI native systems that require standardized protocols.

Does MCP support no code or low code server creation?

Emerging platforms in the MCP ecosystem are moving toward no code and low code solutions for server generation. These tools allow users without deep technical expertise to visually build and deploy functional MCP servers democratizing access to AI integration. By converting OpenAPI specifications or simple documentation into runnable servers these platforms lower the barrier to entry for businesses looking to experiment with AI agents. This trend toward automation in server creation suggests that MCP will eventually become as ubiquitous and easy to deploy as modern web widgets further driving adoption across various industries and use cases.

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