Why MCP Registries Are Failing You (And What To Use Instead)
There are 17+ MCP registries and directories. None of them tell you which servers are actually good. Here's why the registry model is broken and what the alternative looks like.
You need an MCP server for database access. You search "MCP database server" and get results from mcp.so, mcpservers.org, mcpmarket.com, LobeHub's directory, awesome-mcp-servers on GitHub, and a dozen other registries you have never heard of. Each one shows you a different list. None of them agree on which server is best. None of them tell you if the server you are looking at actually works, is maintained, or is worth the token cost.
This is the MCP discovery problem, and it is getting worse as the ecosystem grows.
The Discovery Problem Nobody Has Solved
The Model Context Protocol has no built-in discovery mechanism. There is no npm search for MCP servers, no centralized package registry with version tracking and download counts, no way to query "what servers exist for this use case" from within your AI client.
GitHub issue #561 on the MCP specification repository proposes federated discovery -- essentially an RSS-like mechanism where servers could announce themselves and clients could find them automatically. It is a good idea. It is also not implemented yet, and may not be for some time.
In the meantime, the vacuum has been filled by at least 17 competing registries and directories. Some are community-driven GitHub repositories. Some are venture-backed startups trying to become the "npm of MCP." Some are side projects that were last updated three months ago. The landscape includes mcp.so, mcpservers.org, mcpmarket.com, LobeHub's MCP directory, Smithery, PulseMCP, the awesome-mcp-servers list on GitHub, and many others.
None of them are authoritative. None of them have won. And none of them solve the actual problem developers face: not "does this server exist?" but "is this server any good for my specific workflow?"
Why the Registry Model Is Broken
The fundamental issue is not that registries exist. It is that the registry model -- list everything, let users sort it out -- is the wrong approach for MCP servers. Here is why.
No quality filtering
Registries optimize for coverage, not quality. They want to list every MCP server they can find because a larger catalog looks more impressive. The result is that a weekend project someone abandoned after two commits sits in the same list as a production-grade server maintained by Stripe's engineering team.
When you search for "database MCP server," you might get 30 results. Five of them work reliably. Three of those are maintained. One of them is actually good for your use case. But the registry presents all 30 as equal options, leaving you to figure out which is which through trial and error.
Some registries show GitHub stars as a proxy for quality. Stars measure popularity, not reliability. A server with 2,000 stars and 47 open issues with no maintainer response is not a better choice than a server with 200 stars and a responsive maintainer who ships weekly updates.
Stale data everywhere
Most registries scrape GitHub once and display the results with minimal updates. The star count you see might be weeks old. The "last updated" date might not reflect whether the server actually works with the current version of the MCP protocol. Download counts, where they exist at all, are often aggregated from npm in ways that do not distinguish between automated CI installs and real human usage.
MCP is evolving rapidly. A server that worked perfectly six months ago might be broken today because the protocol specification changed and the maintainer did not update. Registries rarely surface this kind of maintenance status information because it requires active monitoring, not just a one-time scrape.
No workflow context
This is the deepest problem. Registries organize servers by category -- database, search, monitoring, file system. They do not organize them by workflow. A frontend developer searching for useful MCP servers does not want to scroll past 40 database servers, 15 DevOps servers, and 20 data science servers to find the four or five that are relevant to building user interfaces.
Knowing that a server exists is not the same as knowing whether it belongs in your stack. The answer to "should I use this server?" depends on what you are building, what other servers you are already running, how many tokens you can afford to spend, and whether this server overlaps with something you already have configured. No registry provides this context.
No configuration help
Even after you find a server, you are only halfway there. You still need to configure it for your specific AI client. The JSON format for Claude Desktop is different from Cursor, which is different from Windsurf, which is different from Cline. Each client has its own quirks around environment variable handling, transport configuration, and argument formatting.
Registries typically show you the server's README, which might include a configuration example for one client. Maybe two if you are lucky. If you are using a different client, you are on your own to translate the format.
When Discovery Becomes an Enterprise Problem
The discovery problem is not just an inconvenience for individual developers. It is becoming a genuine security and compliance concern for companies.
Salt Security launched an entire product -- Salt MCP Finder -- specifically to help enterprise teams discover and inventory the MCP servers running across their organization. The fact that a security company built a dedicated tool for MCP server discovery tells you everything you need to know about the state of the ecosystem. When companies literally cannot track what MCP servers their developers are running, the discovery problem has moved from "annoying" to "risk."
Enterprise teams need to know which servers are accessing production databases, which ones have access to payment APIs, and whether any of them have known vulnerabilities. Registries do not track any of this. They list servers. What happens after installation is someone else's problem.
The Alternative: Curated Stacks
The registry model assumes you want to search through a catalog and pick individual servers. The curated stack model makes a different assumption: you want a working set of servers designed for your specific workflow, tested for compatibility, and ready to configure in seconds.
Here is how stacks solve each of the registry problems:
Quality filtering is built in. Every server in a stack has been evaluated for reliability, maintenance status, and token efficiency. A server does not get included because it exists. It gets included because it is the best option for that particular role in that particular workflow.
Data stays current. Instead of scraping once and forgetting, a curated approach means actively monitoring the servers in each stack. When a server's maintenance drops off or a better alternative appears, the stack gets updated.
Workflow context is the starting point. Instead of searching by category, you start with your role. Are you a frontend developer? An indie hacker? A DevOps engineer? Each stack is assembled for a specific workflow, so every server you see is relevant to what you actually do.
Configuration is included. Every stack comes with ready-to-paste configurations for all major AI clients. You select your client, enter your API keys, and copy the config. No JSON editing, no format translation, no guessing at argument flags.
How StackMCP Approaches Discovery
At stackmcp.dev, we started from a simple premise: the unit of discovery should be the workflow, not the individual server. When someone asks "what MCP servers should I use?", the right answer is never a list of 10,000 options. It is "here are the four servers that match what you do, here is why each one is there, and here is the config."
Every server in our catalog gets a quality score from 0 to 100 based on maintenance activity, documentation quality, error handling, and community health. This is not a GitHub star count. It is an assessment of whether the server is production-ready and well-maintained.
We show token impact upfront for every server and every stack. Before you add a server to your setup, you know exactly how many tokens it will cost you. This matters because, as we have written about before, an overloaded MCP configuration actively degrades your AI assistant's performance.
Tools like the Stack Builder let you customize if the pre-built stacks do not fit your exact needs. Start with a curated stack, add or remove servers, and the config generator handles the rest.
The Future of MCP Discovery
The MCP specification will likely adopt some form of standardized discovery. The proposals in discussion -- federated registries, server manifests, capability advertisement -- draw on patterns that have worked for other protocols. RSS for blogs, DNS for domains, package registries for libraries. MCP will eventually have its equivalent.
When that happens, the raw discovery problem will get easier. Finding servers will become a protocol-level feature rather than a third-party service.
But discovery is only the first step. Knowing that a server exists does not tell you whether it is good, whether it fits your workflow, whether it overlaps with servers you already have, or how to configure it for your editor. Those are curation problems, and they will persist regardless of how the protocol evolves.
The registries that exist today are filling a temporary gap in the protocol with a model that does not serve developers well. The future belongs to opinionated, quality-filtered, workflow-oriented approaches that respect your time and your token budget.
If you are tired of scrolling through registries and guessing which servers are worth your time, browse the curated stacks on stackmcp.dev. Each one is a tested, optimized starting point for a specific workflow -- ready to configure in under a minute.