As organizations adopt Model Context Protocol (MCP) servers to extend and customize their AI systems, a common architectural question arises: How do you organize servers by domain while still making them discoverable and usable across the enterprise?
The promise of MCP servers is modularity: each server encapsulates a domain’s knowledge, tools, or APIs. For example, Finance may host an MCP server that exposes forecasting models, while HR may host another that provides policy information. This domain-oriented approach keeps ownership clear and supports scaling, but it also introduces a discovery problem:
- How do employees and applications know which servers exist?
- How do they connect to the right one without manually maintaining configuration files?
- How do you ensure governance while still encouraging adoption?
The Discovery Problem in Context
Discovery challenges emerge whenever you decentralize services: too much centralization creates bottlenecks, but too much fragmentation leads to silos. With MCP servers, this tension is magnified because they’re meant to be “pluggable” into AI assistants, applications, and workflows. If users don’t know what’s available—or can’t connect reliably—value is lost.
Common symptoms of poor discovery:
- Duplicate servers exposing overlapping capabilities.
- Users requesting new servers that already exist.
- Shadow integrations bypassing governance because discovery was too hard.
Patterns for Solving MCP Discovery
1. Central Registry or Directory Service
Create a centralized registry—a catalog of all approved MCP servers in the organization. Each server publishes metadata (name, domain, description, version, endpoints, owner) into the registry. Tools and users can then query this registry to find the right server.
Best practices:
- Automate registration as part of your server deployment pipeline.
- Tag servers with domains (Finance, HR, Operations) and capability keywords.
- Provide APIs and UI search so both machines and humans can discover.
This mirrors how internal API gateways or service meshes solve discovery in microservices.
2. DNS and Naming Conventions
Standardize DNS naming to align servers with domains, e.g.:
finance.mcp.company.comhr.mcp.company.comsupplychain.mcp.company.com
This makes it intuitive to locate a server if you know the domain, while still allowing the registry to act as the authoritative source.
3. Integration with Identity & Access Management (IAM)
Discovery isn’t just what exists—it’s also what you’re allowed to use. Tie the registry to IAM so that when a user searches for servers, results are filtered based on entitlements. This reduces noise and helps with compliance.
4. Self-Service Portals
Think of an “App Store” for MCP servers. A self-service portal allows business users to browse available servers, request access, and see example use cases. This encourages adoption while maintaining governance.
5. Versioning & Deprecation Policies
Without lifecycle management, discovery becomes polluted with outdated servers. Establish clear rules for versioning, deprecating, and removing servers from the registry.
6. Telemetry-Driven Recommendations
Go a step further: use usage analytics to surface “recommended servers.” For example, if users in the Tax department frequently connect to Finance and Payroll servers, suggest these during onboarding. This creates a feedback loop between discovery and adoption.
Example Implementation
- Registry Layer – Built on top of your API management platform or a lightweight database exposed via GraphQL.
- DNS Convention – Map each server’s endpoint using subdomains.
- Authentication & Access – Integrate with your enterprise SSO.
- Portal UI – Create a searchable catalog with ownership metadata, SLAs, and onboarding docs.
- Monitoring – Track adoption metrics to ensure the catalog reflects reality.
Why This Matters
The discovery problem isn’t unique to MCP—it’s been seen in APIs, microservices, and even SharePoint document libraries. What’s different here is the AI-first context: if MCP servers are hard to find, your AI assistants won’t surface the right knowledge at the right time. That directly undermines the productivity and strategic advantage AI is supposed to deliver.
Solving discovery early ensures that your domain-oriented MCP architecture remains a strength, not a liability. It allows you to scale servers across departments while keeping them usable, governed, and impactful.
Bottom Line and Takeaway
The discovery problem is not a side issue. It is the single biggest determinant of whether your domain-oriented MCP strategy succeeds or collapses. Without a clear discovery mechanism, you will create duplication, shadow systems, and a graveyard of unused servers.
Opinionated view: Treat discovery as a first-class product in your architecture. Build a registry with IAM integration, enforce naming conventions, and launch a self-service portal. Anything less is wishful thinking.
If you are serious about MCP as the foundation of your AI ecosystem, then invest in discovery upfront. Organizations that fail here will end up with chaos disguised as modularity. Organizations that solve it will build a scalable, governed, and discoverable layer of intelligence that actually makes AI assistants useful across the enterprise.
Takeaway: The ability to find, trust, and connect to MCP servers is the difference between AI that looks interesting and AI that actually scales. Discovery is not plumbing, it is the product.
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