How to build an enterprise-grade MCP registry

Just as integration catalogs were must-haves at the peak of SaaS, Model Context Protocol (MCP) servers are now becoming all the rage for connecting AI agents and enterprise systems. 

In this paradigm, developers aren’t hand-coding API calls to external systems, nor are users clicking “click to integrate” and entering credentials into GUIs. Instead, agentic systems are looking up available MCP servers and making MCP tool calls autonomously. To guide this process, MCP registries are emerging as a way to catalog available MCP servers and guide AI agent workflows.

“MCP registries are increasingly becoming the integration catalog for agentic systems,” says Ebrahim Alareqi, principal machine learning engineer at Incorta, provider of an open data delivery platform. “They give developers and platform teams a centralized inventory of the tools, agents, and capabilities available to an organization.”

MCP registries shorten time to integration and act as a discovery point for AI agents. But whether you’re repurposing an off-the-shelf MCP registry or building your own, the job comes with daunting technical challenges, like figuring out semantics for tool discovery and adding in guardrails for safe autonomous usage.

“A good MCP registry is more than a directory of tools,” says Derek Ashmore, agentic AI enablement principal at Asperitas Consulting, a cloud computing consultancy. “It’s part of your control plane.” For Ashmore, an MCP registry needs strong identity and discovery, policy-aware metadata, life-cycle controls, security guardrails, and data to inform observability.

Below, we’ll dive deeper into what makes up a solid, functional MCP registry. We’ll explore the features and requirements of MCP registries, examine the emerging implementation advice for enterprises, and determine when you should use private versus public MCP registries.

What is an MCP registry?

An MCP registry acts as a single source of truth for MCP servers. It’s a catalog of approved, compliant MCP servers and MCP tools that are available within an organization that can be exposed to AI agents. By pointing AI agents to an MCP registry endpoint, an enterprise can equip AI workflows with actionable read-write access across engineering, business, and SaaS systems that are sanctioned and configured for company use.

To date, there are a handful of public MCP registries out there, ranging from open directories to curated registries and more enterprise-ready implementations. The most obvious example is the official MCP Registry, an open-source catalog of MCP servers with a live REST API for search and discovery. The official MCP registry aligns with the MCP registry specification, which provides a standardized method to build interoperable MCP registries.

Other public resources are more like static lists, such as directories from MCP.so, to Glama.ai, Mastra.ai, and OpenTools. Interestingly, one open-source tool, MCP-Get, provides a command-line option for interaction.

More and more digital services are beginning to embed MCP catalogs into their platforms, too. Docker and Microsoft, for instance, are building curated MCP catalogs focused on their own platform ecosystems. GitHub hosts a directory of MCP servers for easy installation, and has begun to add controls for internal registry configurations. The MACH Alliance, an industry consortium focused on composable commerce, is also promoting an MCP-compliant registry initiative.

Beyond these efforts, MCP registries are moving beyond public directories — enterprises are now constructing private, self-hosted registries for governed internal MCP use. While examples of private enterprise MCP registries are nascent, they are undoubtedly being aided by emerging features in infrastructure platforms.

Take the MCP Center, powered by the Azure API Center, which demonstrates how to build MCP registries in Azure. Lunar.dev’s Custom MCP Server Registry also allows admins to create their own scoped internal MCP registries.

The benefits of an MCP registry

“The biggest benefit of an MCP registry is discoverability,” says Justin O’Connor, founder at Infracodebase, an agentic platform for cloud infrastructure, which hosts a public MCP registry for connecting AI agents to cloud providers.

“MCP servers often end up scattered across teams and systems, so a registry gives you one clear place where people can find what exists,” says O’Connor. This allows AI agents to discover tools with less trial and error.

Others agree that improved discovery is a much-needed element for autonomous agents. “MCP is designed to ensure agents have enough context to generate the right response, and registries are a natural extension of that,” says Incorta’s Alareqi.

A well-constructed MCP registry brings uniformity that aids adoption, reuse, and governance, says O’Connor, because it can be treated as an official inventory of approved capabilities. As such, MCP registries serve a similar function to package registries for software, he adds. 

In this way, an MCP registry can act as a source to vet and update MCP servers before exposing them to agents. Although an MCP registry doesn’t replace core authentication requirements for each MCP server, it does aid provenance and supply chain security, says O’Connor.

Core elements of an enterprise-grade MCP registry

While they share similarities with traditional software integration catalogs, MCP registries have some unique elements. “If you are treating the AI or MCP registry as just another static catalog, you’re doing it wrong,” says Christian Posta, VP and global field CTO at Solo.io, a cloud-native infrastructure company.

Many elements make up a high-quality MCP registry beyond a static tool catalog. In general, they can be boiled down to rich tool metadata, features for developers, and enhanced security guardrails. The effectiveness of an MCP registry will also depend on underlying MCP and API security best practices.

Rich tool metadata

First, an MCP registry needs the bread-and-butter details required to function with MCP. “A solid MCP registry needs to support the basics required by the protocol,” says O’Connor of Infracodebase. This includes how to connect to a server, the transport type, server URL, and configurations required, like environment variables or secrets.

Next are details to aid tool discovery. An MCP registry must provide methods for AI agents to automatically discover the appropriate underlying MCP tools. 

According to Posta, making tools discoverable requires resources that enable semantic search, such as embeddings of the tool name, description, and input schema, along with clear summaries. Ideally, he adds, this experience layer supports progressive disclosure to optimize context windows.

“Agents need context,” adds Incorta’s Alareqi. “Metadata around capabilities, schemas, side effects, cost, latency, and failure modes, to name a few, is what allows an agent to choose the right tool.”

William Collins, director of tech evangelism at Itential, provider of an infrastructure orchestration platform, also sees semantic cues as necessary for discovery, along with other metadata. These should flag rich semantic metadata beyond endpoint descriptions, versioning with breaking-change signaling, and clear capability scoping, he says.

Developer controls

Although agents will use MCP registries programmatically, the registries still must be maintained by human developers (most likely by platform engineers). The MCP registry should therefore provide controls to add new servers, remove them, and set privileges.

To streamline this, a key aspect of an effective MCP registry is good developer experience, says Ido Halevi, director of product management at Silverfort, an identity security company. “That means clear documentation, examples of usage from other teams, and reliability signals such as active maintenance and adoption across agents,” Halevi says.

A strong registry also provides context beyond being a basic tool list. “Teams need to know whether an MCP server is maintained, how widely it’s used, and what kinds of risks or privileges it requires,” says Jessica Kerr, engineering manager of developer relations at Honeycomb, an observability platform provider. For instance, Kerr suggests adding lightweight moderation controls to flag dependable versus experimental MCP servers.

Security guardrails

Since the concept of MCP registries is so new, security standards and guidelines are still emerging. “It’s a bit like the wild west,” says Gil Feig, co-founder and CTO of Merge, provider of a unified API platform. 

Because of this, Feig emphasizes the need for strong security guardrails and privilege boundaries. “When evaluating an MCP registry, look for one that offers robust authentication, observability, and data governance with built-in rules, proactive alerts, and real-time logs,” he says.

The authorization context will especially matter to ensure that agents are using MCP tools permitted by the organization and have authorized access to sensitive material. As such, MCP registries will require information on the agent identity, its intent, and what user it’s acting on behalf of, says Posta. 

“Registries should favor servers that properly separate user sessions so data does not leak between users,” adds O’Connor, who notes that support for per-user authentication using modern OAuth patterns helps ensure access that is matched to privileges.

Similarly, Halevi underscores the need for enforcement beyond pure tool discovery. “Without enforcement, all you’re doing is cataloging risk,” he says. A registry should help control which agents can access which tools, and dynamically enforce permissions when a tool is invoked.

Native API handling underneath

Native API handling notwithstanding, there’s only so much a registry can do. Core authentication nuances will differ from MCP server to MCP server, and each will require the same security rigor as a standard API connection.

“At the server level, MCP servers must be built with robust security capabilities from the ground up,” says Alex Salazar, co-founder and CEO of Arcade.dev, the maker of an AI tool calling platform. An MCP registry doesn’t replace core MCP server security basics such as OAuth-based authentication, proper token and secrets handling, and observability.

“The issue here is many AI applications don’t have any native API handling in place,” adds Melissa Ruzzi, director of AI at AppOmni, a cybersecurity company. “So they look to the MCP registry as a way to control MCP authentication, which is not a good practice.” 

Others aren’t certain guardrails belong at the registry level to begin with. “Security guardrails and privilege boundaries are really the responsibility of the underlying agents and not the best function of a registry-as-exchange,” says Dan Fink, AVP software architect at Cognizant, an enterprise technology consulting firm.

To really enforce this, adds Fink, you’d require additional layers that would either be too heavy, like introducing entirely new agents as intermediaries, or just simple guardrail tags, which could easily be faked or obsoleted.

For this reason and others, some view the MCP registry itself as more of an abstraction layer, which only defines high-level capabilities that are then mapped to underlying scopes, roles, and APIs. 

“Registries should express guardrails so orchestration layers can enforce them,” says Itential’s Collins. “This way, the registry doesn’t become a bottleneck and single point of failure.” For Collins, guardrails to enforce at the registry include privilege boundaries, authentication requirements, and risk classifications.

“An enterprise MCP registry should be slightly abstracted, not one-to-one with every tool privilege,” says Derek Ashmore, agentic AI enablement principal of Asperitas Consulting, a cloud computing consultancy. A thin abstraction layer, as opposed to one that directly mirrors every underlying permission, also enables you to standardize permission names across tools, reuse role templates, and separate user types, he adds.

Life cycle and performance

As a tagalong to security guardrails, an MCP registry is an opportune location to introduce supply chain security features and monitoring.

“This includes vetting servers before they’re discoverable, implementing security scans and vulnerability checks, and controlling what can be published or discovered,” says Alex Salazar, co-founder and CEO at Arcade.dev, provider of an AI tool calling platform. Salazar says that registries should track performance metrics and errors, as well.

In addition to dynamic tool discovery and tooling governance, Marco Palladino, CTO and co-founder of Kong, provider of a cloud-native API platform, sees observability across the AI data path as necessary for an enterprise-grade MCP registry.

“Enterprises need centralized visibility into tool usage, health, and failures to support monitoring, optimization, cost management, and compliance,” says Palladino. “Without this, organizations face fragmented integrations and increased operational risk.” 

Beyond the above areas, experts foresee that other attributes will be necessary for MCP registries in an enterprise context: 

• Fingerprinting of the tools within a particular server
• A bridge between private and public registries
• Ranking or scoring based on previous performance, token cost, and other attributes
• Namespace verification to prevent naming conflicts
• Validation layers to catch errors
• Health monitoring to track server availability and performance 

Choosing a public or private MCP registry 

When implementing an MCP registry, organizations have two options: either use a public MCP registry or create a private self-hosted MCP registry. According to the experts, there are trade-offs between each approach.

“A public MCP registry has to be very well evaluated for possible security risks before use,” says Melissa Ruzzi, director of AI at AppOmni, a cybersecurity company. Private registries are generally safer, she says, but the degree of risk depends on how they are implemented.

“The public registry ecosystem is still immature,” says Kevin Cochrane, CMO at Vultr, a cloud hosting provider. “We likely need a ‘Hugging Face for MCP’ — a trusted authority that can validate listings and set consistent standards.” Without that sort of layer, teams should be cautious about smaller third-party registries, he adds. 

Instead, a private MCP registry can help an enterprise govern its portfolio. “Put a private MCP registry at the heart of the AI runtime,” Cochrane says. “This should be core infrastructure owned by platform engineering, with governance over how MCP servers are built, tested, deployed, and monitored.”

Infracodebase’s O’Connor adds that such curated registries engender trust in specific tools. “Over time, registries also become a trust boundary, especially in public settings, because they shape what tools people are willing to bring into workflows,” he says.

For many, the starting point will likely be a combination of both. This could equate to forking a sample open-source MCP registry and extending it to your needs. 

“Another way is to take a published OpenAPI specification and generate a skeleton service implementation in a language of your choice,” says Andrei Denissov, associate director of software engineering at Cognizant AI Lab, the AI research arm for Cognizant.

Tips on building MCP registries

Experimentation with MCP registries is in its early days. However, developers on the front lines are already pulling out lessons learned and discovering patterns for both good and bad designs. 

One lesson is the sheer realization that you need registries, quicker than you think. “Working with teams deploying MCP at an enterprise scale, the pattern is consistent: Registries become necessary faster than organizations expect,” says Ido Halevi, director of product management at Silverfort, an identity security company. 

Then, those implementing MCP registries quickly learn that a basic MCP catalog is only one part of the picture — enterprises need much more than just MCP tool discovery. They need per-agent authorization models, guaranteed human-linked attribution, deep observability into agent behavior, and inline enforcement,” says Halevi.

When operating many MCP servers at scale, other requirements beyond discovery begin to become just as important, adds Halevi, such as MCP server orchestration, managing keys, keeping versions aligned, and managing configuration changes.

Balancing agentic autonomy and control

In the enterprise, sanctioned MCP use is proving to be incredibly powerful. Just take the case of Workato, which experienced a 700% increase in Claude chats from internal employees over a 60-day period when it turned on enterprise MCP features. Support engineers, financial analysts, sales leads, and others are building new workflows that grow Workato’s business in tangible ways, much in part thanks to MCP.

Getting those results, however, requires balancing agentic autonomy with control. That’s where an MCP registry can shine. For an enterprise, the quality of an MCP registry doesn’t just depend on listing every MCP server in a directory. It hinges on trust, safety, and smart controls — especially to prevent leaking data from chat streams across inter-organizational agent workflows, for instance.

As such, enterprises going “all in” on MCP should seriously consider MCP registries as a core infrastructure, with all the standard architectural enterprise bells and whistles. “It should be treated like any other serious piece of software,” says Alareqi. “That means strong versioning, life-cycle management, and observability.”