Building an MCP Registry: Why It Matters and How to Get It Right

A while back, I wrote about the concept of an enterprise MCP Gateway, along with key considerations for securing MCP servers. Building on that, I want to take a closer look at the role of an MCP Registry (or Catalog) in delivering MCP as an enterprise service.

While working on Obot, we needed a practical, scalable way to implement a registry—and it quickly became clear that a well-designed registry is fundamental to secure, discoverable, and compliant AI infrastructure.

In this post, I’ll briefly cover what an MCP registry is, why it matters, how to approach building one, and what’s evolving in the open-source community, including the MCP Registry project.

What Is an MCP Registry?

An MCP registry is a centralized catalog of all the MCP servers available within an organization. It serves as a directory of IT approved MCP servers that users can evaluate and provision for their own use cases. Think of it like public MCP directories – like Pulse MCP or MCP.so – but with enterprise-grade security, governance, and discoverability.

A robust enterprise MCP registry includes:

• What MCP servers are approved within the enterprise
• Who created them or is responsible for them
• What they do (with live documentation and tool descriptions)
• Who can access them (with role-based permissions)
• How to connect (with unique URLs for each client or tool)

Why Build an MCP Registry?

Think of MCP servers as a new internet of capabilities for AI agents and user chat clients. Without a registry, that internet is basically offline and running on local machines not available to your organization. AI infrastructure quickly becomes fragmented. Teams spin up their own MCP servers, integrations go undocumented, and IT loses visibility. The result? Shadow AI, data leaks, compliance headaches, and wasted time as users struggle to find or trust the right endpoints.

A well-implemented MCP registry solves these problems by providing:

• Centralized control: IT can publish, manage, and secure every MCP in one place.
• Frictionless discovery: Users find the right AI tools quickly and confidently.
• Auditability: Every access and change to an MCP server can be logged for compliance.
• Scalability: Onboarding new MCPs becomes a repeatable, secure process.
• Flexibility: Both internally hosted and third party MCP servers can be added and managed

How an MCP Registry Works: Architecture and Key Components

Registry API and Metadata Model

At the core of an MCP registry is a catalog exposed through an API, typically defined using an OpenAPI specification. This API allows developer tools, AI clients, and internal platforms to query the registry and discover available MCP servers.

Each server entry contains structured metadata such as:

• Server endpoint URL
• Supported capabilities or tools
• Version information
• Supported transport protocols
• Additional descriptive fields

The registry performs basic validation to maintain catalog integrity. For example, it ensures namespaces are unique and metadata follows the expected schema. This helps keep the directory reliable and machine-readable.

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Namespace and Ownership Verification

To prevent impersonation or naming conflicts, registries can verify that the entity publishing an MCP server controls the namespace associated with it.

Possible approaches include:

• GitHub-based namespaces (for example io.github.username/*) verified through OAuth authentication.
• Domain-based namespaces (for example com.example/*) validated through DNS records or HTTP verification challenges.

These verification mechanisms ensure that only legitimate owners can publish servers under a specific namespace, improving trust across the ecosystem.

Federation and Sub-Registries

A key architectural decision behind MCP registries is federation. The official registry is not intended to be the only registry. Instead, it acts as a canonical source of public MCP server metadata.

Other registries can build on top of it.

Examples include:

• Public sub-registries that enrich metadata with ratings, search filters, or audit information.
• Enterprise registries that combine public MCP servers with internally developed ones.

Enterprise registries can also apply their own policies, governance controls, and permission systems while still exposing a compatible API to MCP clients. To maintain interoperability, these sub-registries typically reuse the same OpenAPI schema and metadata structure defined by the upstream registry.

Remote Server Discovery and Connection

The registry is responsible for discovery, not execution. It does not run MCP servers or proxy runtime traffic.

Instead, it stores metadata describing how to reach each server, including:

• Endpoint URLs
• Supported transport methods
• Authentication requirements

Using this metadata, MCP clients can connect directly to MCP servers using standard MCP transport mechanisms such as:

• Streamable HTTP
• Server-sent events (SSE)

Server metadata may also describe required authentication headers or credentials so clients know how to authenticate before connecting.

Trust, Validation, and Governance

While the registry primarily validates metadata and namespaces, additional trust layers can be implemented by downstream systems.

For example:

• Sub-registries may verify server manifests or signing keys.
• Clients may validate server responses or tool payloads.
• Enterprises may apply additional security checks before allowing internal access.

Governance and moderation are also important. Registry maintainers or administrators can review entries, respond to reports of malicious servers, and remove problematic listings when necessary.

This hybrid model combines automated validation with human oversight to maintain trust in the catalog.

Performance, Scalability, and Evolution

Because MCP registries store metadata rather than large software packages, they can scale efficiently.

Common techniques used to support large catalogs include:

• Pagination for large result sets
• Caching strategies using TTLs or conditional requests
• Efficient search and filtering

The metadata schema is also versioned. This allows the registry to evolve its structure over time while maintaining backward compatibility with existing MCP clients and tools.

What Is an MCP Registry’s Role When Using an MCP Gateway?

An MCP gateway is the entry point through which users and clients securely access MCP servers. It acts as a reverse proxy and policy enforcement layer, sitting between the client and the target MCP. The gateway handles authentication, authorization, request routing, and telemetry collection. It can inject identity headers, apply rate limits, and ensure that only approved traffic reaches registered MCP servers.

An MCP registry is the heart of the MCP gateway. It powers:

• The user catalog: Employees browse, search, and connect to MCPs they’re authorized for.
• Connection management: The gateway generates unique, secure URLs for each user and client.
• Policy enforcement: Access controls and audit logs are tied directly to registry entries.
• Monitoring and analytics: Usage stats, health checks, and compliance reporting all flow from the registry.

Without a strong registry, the gateway is just a proxy. With it, you have a true control plane for enterprise AI.

How to Build Your Own MCP Registry

1. Choose the Right Platform
An MCP registry can stand alone or be part of a broader MCP Gateway. When we started building an MCP registry, we realized that it worked best when it was connected with other features like access control, MCP hosting, an MCP Proxy, and more. We built all of that into the Obot MCP Gateway, and made it open-source. With the right platform, the registry is a key component of your MCP delivery strategy.
2. Define Metadata Standards
A registry is only as useful as the information it holds. For each MCP, it’s important to track:

• Name and description
• Owner/maintainer
• Endpoint URL(s)
• Supported models and capabilities
• Documentation
• Trust level (IT-verified, experimental, etc.)
• Access policies (who can use it, and how)

3. Automate Onboarding
When you have 20 MCP servers you can manage them through a UI. But as you scale, a GitOps workflow works much better: teams submit a pull request with a new MCP’s metadata, which triggers automated review and, once approved, adds it to the registry.
4. Enforce Access and Security
Every MCP in the registry should be behind a proxy that enforces strict authorization policies. The registry acts as the gatekeeper—no one connects to an MCP without going through the gateway, which checks permissions and logs every request.
5. Keep the Registry Up to Date
Regular reviews help prune unused MCPs, update documentation, and verify trust levels. The registry should be a living part of the AI ecosystem, not a static list.

What Is the Official MCP Registry?

The official MCP Registry is a centralized metadata service for publicly accessible MCP servers. It is currently in preview, which means its structure and data may change before general availability. The project is backed by major contributors in the MCP ecosystem, including Anthropic, GitHub, Microsoft, and others. Its primary role is to provide a single, authoritative place where server creators can publish structured metadata describing their MCP servers.

Source: modelcontextprotocol.io

The registry focuses on metadata, not code. It stores standardized server.json documents that describe how to locate and run a server, including package references (such as npm or Docker), execution details, and declared capabilities. The actual server code remains in external package registries like npm or PyPI. This separation keeps the registry lightweight while allowing it to act as a discovery layer that maps logical server identities to their underlying implementations.

The MCP Registry is designed as part of a broader ecosystem rather than a standalone tool for direct consumption. Downstream aggregators and marketplaces pull data from it, enrich it with additional context like ratings or curation, and expose it to end users and clients. It also defines a standard OpenAPI interface that other registries can implement, enabling interoperability across public and private catalogs. Trust is enforced through namespace verification, ensuring that only legitimate owners can publish under a given identity.

Tutorial: Publish an MCP Server to the Official MCP Registry

This tutorial walks through the process of publishing an MCP server to the MCP Registry using the official mcp-publisher CLI. The registry stores server metadata, not the server code itself, so the package must first be published to npm before registering it.

Prerequisites

Before starting, make sure you have the following:

• Node.js installed (the example assumes a TypeScript MCP server)
• An npm account to publish the server package
• A GitHub account for authentication with the MCP Registry

If you do not already have an MCP server, you can use the TypeScript weather server example from the MCP quickstart repository:

git clone --depth 1 git@github.com:modelcontextprotocol/quickstart-resources.git  cp -r quickstart-resources/weather-server-typescript .  rm -rf quickstart-resources  cd weather-server-typescript

Update the package.json file so it contains your npm package name, repository URL, and description.

Step 1: Add Verification Information to the Package

The MCP Registry verifies that a server’s package matches the metadata being published. To enable this, add an mcpName property to your package.json.

Example:

{   "name": "@my-username/mcp-weather-server",   "version": "1.0.1",   "mcpName": "io.github.my-username/weather",   "main": "index.js"  }

The mcpName value becomes the server’s identifier in the MCP Registry. When using GitHub authentication, it must start with:

io.github.<your-github-username>/

Step 2: Publish the Package to npm

Because the MCP Registry only stores metadata, the server package must be published to npm first.

Build the project and install dependencies:

cd weather-server-typescript  npm install  npm run build

Authenticate with npm if needed:

npm adduser

Then publish the package:

npm publish --access public

After publishing, verify the package is available by visiting its npm page, for example:

https://www.npmjs.com/package/@my-username/mcp-weather-server

Step 3: Install the MCP Publisher CLI

The MCP Registry provides a command-line tool called mcp-publisher for publishing server metadata.

Install it using a prebuilt binary or a package manager such as Homebrew. Once installed, verify it works:

mcp-publisher --help

You should see commands such as:

init  login  logout  Publish

These commands are used to create metadata, authenticate, and publish servers to the registry.

Step 4: Create the Server Metadata File

Each MCP server is described using a server.json metadata file. The CLI can generate a template automatically.

Run the following command inside your server project directory:

mcp-publisher init

This creates a server.json file with basic information derived from your project.

A typical example looks like this:

{   "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",   "name": "io.github.my-username/weather",   "description": "An MCP server for weather information.",   "repository": {     "url": "https://github.com/my-username/mcp-weather-server",     "source": "github"   },   "version": "1.0.1",   "packages": [     {       "registryType": "npm",       "identifier": "@my-username/mcp-weather-server",       "version": "1.0.1",       "transport": {         "type": "stdio"       }     }   ]  }

Make sure the name field matches the mcpName value in package.json. Adjust the version and other fields as needed.

Step 5: Authenticate With the MCP Registry

Before publishing metadata, authenticate with the MCP Registry.

Run:

mcp-publisher login github

The CLI will display a GitHub device authentication flow. It provides a URL and a temporary code. Open the link, enter the code, and authorize the application.

Once completed, the CLI will confirm that authentication was successful.

Step 6: Publish the Server to the MCP Registry

After authentication, publish the server metadata using:

mcp-publisher publish

The CLI uploads the server.json file to the MCP Registry. If the process succeeds, the output will confirm the server name and version that were published.

You can verify the server appears in the registry by querying the API:

curl "https://registry.modelcontextprotocol.io/v0.1/servers?search=io.github.my-username/weather"

The response should include your server metadata, confirming that the MCP server is now discoverable through the MCP Registry.

Best Practices for Managing MCP Registries

1. Treat MCP Servers as Governed Software Artifacts

Organizations should manage MCP servers using the same lifecycle governance practices used for traditional software components. This means applying version control, traceability, and approval workflows to every MCP server registered in the system.

Treating MCP servers as governed artifacts improves visibility and accountability across AI-enabled integrations. Teams can track where servers originate, how they evolve, and which versions are deployed across development, CI/CD, and production environments. This approach also reduces operational risk by ensuring that only validated and approved services are introduced into engineering workflows.

2. Implement Strong Access Controls and Least-Privilege Policies

Access to MCP servers should follow the principle of least privilege. Developers, automation agents, and AI clients should only be able to connect to the specific MCP servers required for their tasks.

Restricting access reduces the risk that compromised tools or unauthorized automation workflows can interact with sensitive systems such as repositories, infrastructure platforms, or internal databases. Role-based access policies and permission controls allow organizations to define who can access specific servers, which tools they can invoke, and in which environments they can operate.

3. Enforce Governance Through CI/CD Integration

Governance should be embedded directly into software delivery pipelines. By integrating the MCP registry with CI/CD workflows, organizations can automatically validate MCP servers before they are deployed or used by AI systems.

Policy checks during build and deployment stages can detect vulnerabilities, configuration issues, or unapproved integrations early in the delivery lifecycle. This ensures that only trusted MCP services are available to development teams and production systems, reducing the risk of introducing unsafe automation capabilities into operational environments.

4. Maintain Continuous Monitoring and Usage Visibility

Monitoring registry usage is essential for maintaining governance as AI adoption grows. Continuous monitoring provides visibility into how MCP services are consumed across teams, environments, and automation workflows.

Usage telemetry helps identify outdated integrations, suspicious access patterns, or unauthorized services that bypass governance controls. Operational visibility also allows platform teams to understand adoption trends and evaluate which AI integrations deliver the most value across engineering workflows.

5. Improve Supply Chain Transparency With Dependency Tracking

MCP registries should support transparency mechanisms similar to those used in modern software supply chains. Practices such as maintaining Software Bills of Materials (SBOMs) improve visibility into dependencies and integration relationships across MCP services.

By documenting how MCP servers interact with enterprise systems and automation workflows, organizations gain better insight into the AI integration layer of the software supply chain. This transparency strengthens governance and helps teams respond more quickly to vulnerabilities, misconfigurations, or compromised components.

6. Align Registry Governance With Established Supply Chain Security Frameworks

To strengthen security and audit readiness, organizations should align MCP registry practices with established software supply chain frameworks such as Supply-chain Levels for Software Artifacts (SLSA) and SBOM initiatives.

These frameworks improve traceability, integrity verification, and compliance across development ecosystems. Applying them to MCP registries ensures that AI integrations are governed using the same security standards already used for containers, packages, and other software artifacts.

7. Integrate the Registry With the Broader SDLC Platform

An MCP registry provides limited value if it functions only as a directory of servers. For effective governance, the registry should integrate deeply with the broader software development lifecycle (SDLC) platform.

Integration with development platforms, deployment pipelines, and operational tooling provides the context required to manage AI integrations properly. This context includes project ownership, environment configurations, and deployment stages. With this visibility, organizations can understand which AI agent is calling which tool, in which environment, and for what purpose—enabling true context-aware governance across AI-enabled engineering workflows.

Key Takeaways

• Prioritize the development of a standard MCP registry for your organization
• Define your metadata schema
• Choose a gateway platform (like Obot)
• Automate onboarding and access control
• Make your registry the single source of truth for AI integrations

A well-built MCP registry is foundational for safe, scalable AI adoption. If you want to see what an MCP registry looks like, take a look at Obot Chat and explore some public MCP servers.

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