AI agents are only as capable as the tools they can reach. When an agent needs to query a database, file a support ticket, or pull data from a CRM, it has to find the right tool, authenticate, and invoke it — all at runtime. The Model Context Protocol (MCP) standardizes how agents communicate with these tools. But MCP alone does not answer a fundamental question: how does the agent know which tools exist?

Without a central directory, every agent-to-tool connection becomes a hardcoded, point-to-point integration. Teams duplicate effort. Configurations drift between environments. No one has a clear picture of which agents access which tools. This is the same sprawl that plagued microservices before [API gateways](https://konghq.com/blog/learning-center/what-is-an-api-gateway)API gateways — except now the consumers are autonomous AI agents, not human developers.

An MCP registry solves this. It is a structured catalog — a centralized directory that tells agents what [MCP servers](https://konghq.com/blog/learning-center/what-is-an-mcp-server)MCP servers exist, where they are deployed, and how to connect to them. Think of it as the service catalog for the agentic era. If MCP is the protocol (analogous to HTTP), the registry is the service catalog for agents — a discovery layer whose primary consumers are machines querying programmatically, not humans browsing documentation.

Key takeaways

• An MCP registry is a centralized directory where AI agents discover MCP servers and the tools they expose.
• Registries store metadata — descriptions, capabilities, authentication requirements, and deployment details — not the tools themselves.
• Without a registry, organizations face configuration drift, duplicated integrations, and ungoverned agent access to tools.
• The [MCP Registry Specification](https://modelcontextprotocol.info/specification/)MCP Registry Specification is an open standard; implementations range from the community-run official registry to enterprise-grade solutions with governance built in.
• For enterprises, the registry is where MCP tool discovery meets governance: controlling which agents can access which tools, across which environments.

What is MCP?

[Model Context Protocol (MCP)](https://konghq.com/blog/learning-center/what-is-mcp)Model Context Protocol (MCP) is an open standard that defines how AI agents communicate with external tools and data sources. Rather than building custom integrations for every tool an agent needs, MCP provides a universal interface.

MCP uses a client-server architecture. The AI application — whether a coding assistant, an autonomous agent, or an orchestration framework — runs an MCP client. Each tool or data source exposes its capabilities through an MCP server. The protocol standardizes three operations: tool discovery (what can this server do?), invocation (execute a specific tool with these parameters), and response handling (return structured results back to the agent).

This eliminates the need for bespoke integrations per tool. A single MCP client can communicate with any compliant MCP server, regardless of the tool behind it. [Maintained by Anthropic](https://www.anthropic.com/news/model-context-protocol)Maintained by Anthropic and adopted across the AI ecosystem, MCP is rapidly becoming the default protocol for [agent-to-tool communication](https://konghq.com/blog/learning-center/what-is-an-mcp-agent-gateway)agent-to-tool communication.

For the full primer, see [What is MCP?](https://konghq.com/blog/learning-center/what-is-mcp)What is MCP?

How an MCP registry works

A registry is a metaregistry: it stores metadata about MCP servers, not the server code itself. Each entry describes a server's identity, capabilities, deployment information, and authentication requirements. The registry acts as a catalog layer that agents query to find what they need.

The lifecycle follows three stages: publish, discover, and consume.

Publish. Server authors register metadata describing what their MCP server does, what tools it exposes, and how clients should connect to it. This metadata includes a unique identifier, a human-readable description, a capabilities list, the deployment type, version information, and the authentication method.

Discover. MCP clients — AI agents, IDEs, orchestration frameworks — query the registry to find servers matching their needs. An agent building a customer report might search for servers that expose CRM data access and reporting tools.

Consume. Once a client identifies the right server, it retrieves connection details from the registry and connects directly to the MCP server. The registry facilitates the introduction but stays out of the data path.

Server identity relies on a namespace system. Servers are identified by reverse-domain namespaces — for example, io.github.username/server-name or com.company/tool-name. This convention prevents naming collisions across organizations and verifies ownership. The registry exposes a REST API that agents query programmatically at runtime, with no human intervention required.

The metaregistry model

The registry points to where servers live — npm packages, PyPI distributions, Docker images, remote HTTP endpoints — but does not host the code itself. This keeps the registry lightweight and decoupled from the distribution mechanisms that already exist. Server authors continue publishing through their preferred package ecosystem. The registry simply indexes them.

The analogy is a library catalog versus the books. The catalog tells you what exists, where to find it, and how to check it out. It does not duplicate the books on its own shelves.

Deployment models

MCP servers can be packaged and deployed in two primary patterns.

Local/package deployment. Servers distributed via npm, PyPI, or Docker that clients install and run locally. The MCP client manages the server process directly. This model suits developer experimentation and local tooling workflows.

Remote deployment. Servers hosted as web services, typically using Server-Sent Events (SSE) or Streamable HTTP. Clients connect over the network. This model enables centralized governance because the server runs in infrastructure the organization controls — not on individual developer machines.

Many MCP servers support both models. For enterprises, remote deployment is the critical pattern: it places server execution within managed infrastructure where [access policies](https://konghq.com/blog/product-releases/kong-ai-gateway-mcp-tool-access-controls)access policies, logging, and rate limits can be enforced.

Why AI agents need a registry

Without a registry, every agent-to-tool connection is configured manually. A developer building an agent that needs Slack, Jira, and a database writes three separate connection configurations — specifying endpoints, credentials, and transport details. Another developer building a different agent that needs the same tools writes three more. Multiply this across teams, environments, and dozens of MCP servers, and you have point-to-point sprawl identical to pre-API-gateway integration patterns.

This creates three compounding problems.

Configuration drift. When tool metadata lives in individual agent configurations, there is no single source of truth. One team updates a server endpoint; other teams continue pointing at the old one. Debugging becomes archaeology.

Duplicated effort. Every team independently discovers, evaluates, and configures the same tools. There is no shared catalog to consult.

Ungoverned access. No one has visibility into which agents access which tools. There is no approval workflow, no audit trail, no way to revoke access to a compromised server across all consumers simultaneously. This is shadow AI by default — agents connecting to tools with zero organizational oversight.

A registry eliminates all three. Agents discover tools at runtime by querying the registry, with no code changes or redeployment required. New tools become available to every authorized agent the moment they are published. Retired tools disappear from discovery instantly. The registry becomes the single source of truth for tool metadata and the foundation for governance — controlling who can publish servers and which agents can discover them.

As organizations scale from a handful of MCP servers to dozens or hundreds, manual management becomes untenable. The registry is the infrastructure that makes that scale operational.

MCP registry vs. MCP gateway

The registry and the gateway answer different questions and operate at different layers.

The registry is the discovery layer. It answers: what tools exist, where are they deployed, and what do they do? Agents query the registry to find MCP servers. The registry's job ends once the agent has connection details.

The [MCP gateway](https://konghq.com/blog/learning-center/what-is-a-mcp-gateway)MCP gateway is the enforcement layer. It answers: is this agent authorized to use this tool, and under what constraints? The gateway sits in the traffic path between agents and MCP servers, enforcing authentication, rate limiting, cost controls, PII redaction, and traffic routing at runtime.

A registry without a gateway provides discovery but not runtime governance. Agents can find tools, but nothing prevents unauthorized access or enforces usage policies once they connect.

A gateway without a registry requires hardcoded tool configurations. Agents get governed access to a predefined set of tools, but no dynamic discovery — every new tool requires manual configuration.

The two are complementary. The registry tells agents what is available. The gateway enforces policy when agents connect. In practice, enterprises need both. The value compounds when they share identity, policy, and observability — so approving a server in the registry and enforcing access at the gateway become a single decision. Kong ships both an MCP Registry and an [AI Gateway](https://konghq.com/products/kong-ai-gateway)AI Gateway designed to operate as a unified platform, sharing identity and policy across the discovery and enforcement layers.

Types of MCP registries

MCP registries fall into three broad categories, each suited to different organizational needs.

Open-source and community registries. The [official MCP Registry](https://modelcontextprotocol.io/registry/about)official MCP Registry at registry.modelcontextprotocol.io is community-maintained, fully open, and implements the MCP Registry Specification. It serves as the public directory for open-source MCP servers. Anyone can publish, and anyone can discover. This makes it excellent for public tool discovery and ecosystem growth, but it provides limited governance — there is no access control, no environment separation, and no audit trail.

Industry consortium registries. Vertical-specific registries operated by groups like the [MACH Alliance](https://machalliance.org/mach-alliance-mcp-registry)MACH Alliance focus on particular ecosystems — in MACH's case, composable commerce. These registries curate servers relevant to their domain and provide a vendor-neutral discovery layer for their members.

Enterprise registries. Built for organizations that need to control which tools their agents can access. Enterprise registries add access control, multi-environment support (dev, staging, production), policy-based visibility, allowlisting, and audit trails. They implement the open MCP Registry Specification but extend it with governance capabilities that community registries do not provide.

Organizations that need to control which tools their agents can access — with environment separation, audit trails, and policy-based visibility — require an enterprise-grade registry. Kong MCP Registry, currently available in [Technical Preview](https://konghq.com/blog/product-releases/kong-mcp-registry-tech-preview)Technical Preview, addresses this need. It is fully compliant with the open MCP Registry Specification — not proprietary — while adding Konnect-native authentication, policy-based authorization, allowlisting, per-registry access control, observability, and native integration with Kong AI Gateway for runtime enforcement. For organizations evaluating registries, the decision factors are scale, governance requirements, compliance posture, and multi-cloud needs.

What to look for in an MCP registry

When evaluating MCP registries, six criteria separate solutions that work in production from those that work only in demos.

Spec compliance. Does the registry implement the MCP Registry Specification? Open-standard compliance prevents vendor lock-in and ensures compatibility with the broader MCP ecosystem as it evolves.

Governance and access control. Can you control who publishes servers and which agents discover them? Per-environment separation — so development agents see different tools than production agents — is a baseline requirement for enterprise use.

Observability. Does the registry track tool usage, health, and failures? Visibility into which agents use which tools, how frequently, and whether those tools are performing correctly is essential for operational reliability and cost management.

Gateway integration. Does the registry connect to a gateway for runtime enforcement, or does governance stop at the catalog layer? A registry that shares identity and policy with a gateway closes the loop between discovery and access control.

API-first design. Can agents query the registry programmatically at runtime, or does adding a new tool require human-driven configuration? The entire point of a registry is dynamic discovery — if it requires manual steps, it defeats its purpose.

Federation support. Can you run sub-registries that sync with a primary catalog? Large organizations often need decentralized teams to manage their own tool registries while maintaining a unified view at the platform level.

Frequently asked questions

Is there a registry of MCP servers?

Yes. The official MCP Registry at registry.modelcontextprotocol.io is the community-maintained public registry implementing the open specification. Enterprise alternatives like Kong MCP Registry add governance, access control, and observability on top of the same standard.

How do I create an MCP registry?

The MCP Registry Specification is an open API standard anyone can implement. For most organizations, adopting an existing registry — either the open-source community registry or an enterprise solution — is significantly faster than building and maintaining one from scratch.

What is the difference between an MCP registry and an MCP gateway?

A registry is a discovery catalog that tells agents what tools exist and where to find them. A gateway enforces policy at runtime, controlling how agents access those tools. Most enterprises need both working together.

Can I run a private MCP registry?

Yes. Enterprise MCP registries support private, self-hosted deployments with access control, environment separation, and policy-based visibility. This allows organizations to maintain an internal MCP server catalog that is invisible to the public internet.

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An MCP registry is the infrastructure layer that transforms ad-hoc agent-to-tool connections into a governed, discoverable ecosystem. Without it, every AI agent deployment adds another set of unmanaged, point-to-point integrations. With it, organizations get a single source of truth for tool discovery and the foundation for enterprise-grade governance.

As organizations scale AI agent deployments from pilots to production, the registry becomes foundational — the equivalent of what API gateways became for microservices a decade ago. The organizations that treat agent-to-tool connectivity as an infrastructure problem, rather than an application problem, will scale faster and govern more effectively.

Explore how [Kong MCP Registry](https://konghq.com/products/mcp-registry)Kong MCP Registry brings dynamic tool discovery, enterprise governance, and full observability to your agentic AI infrastructure. Ready to see it in action? [Request a demo](https://konghq.com/contact-sales)Request a demo to explore Kong's API platform capabilities.