---
title: "7 Steps for Taking an MCP Server to Production"
description: "It's easy to spin up an MCP server. What's hard is operationalizing it at scale. Here are the steps to make it production-grade."
canonicalUrl: "https://zuplo.com/blog/2026/10/06/mcp-server-to-production"
pageType: "blog"
date: "2026-10-06"
authors: "billDoerrfeld"
tags: "Model Context Protocol, API Best Practices, ai-agents"
image: "https://zuplo.com/og?text=7%20Steps%20for%20Taking%20an%20MCP%20Server%20to%20Production"
---
MCP is opening new doors for agents and automated integrations. And if you're a
SaaS or developer tool or platform, chances are you're looking into things like
agent skills and MCP, and perhaps you've already crafted an MCP server.

However, making the most out of MCP is more than just wrapping an API with MCP
and throwing it in a
[registry](https://www.infoworld.com/article/4145014/how-to-build-an-enterprise-grade-mcp-registry.html).
There are plenty of steps to consider, from capability design to
[security](/learning-center/securing-mcp-servers-auth),
testing, distribution, promotion, and everything in between.

<CalloutAudience
  variant="bestFor"
  items={[
    `API and platform teams that have a prototype MCP server and need it production-ready`,
    `SaaS providers planning to offer a remote MCP server to customers or partners`,
    `Teams deciding where authentication, monitoring, and billing for MCP should live`,
  ]}
/>

Below, we'll walk through the general steps — including the actual
[server generation](/blog/mcp-server-generators-compared) —
that MCP builders should be considering when they go about operationalizing MCP
servers in practice and at scale.

As you'll see, with some upfront planning, MCP servers will be better poised to
provide more accurate results, optimize token usage, encourage more use, and
secure sensitive endpoints.

## 1. Determine what capabilities to expose

_Before mapping an API one-to-one, follow best practices for tooling design_

When given MCP servers, agents can produce some serious token drains for
consumers.
[Anthropic has found](https://www.anthropic.com/engineering/code-execution-with-mcp)
that, depending on the setup, tool definitions alone can consume 50,000 tokens
before the conversation even begins.

And as the
[AWS blog explains](https://aws.amazon.com/blogs/machine-learning/mcp-tool-design-practical-approaches-and-tradeoffs/),
without designing your tools for how large language models (LLMs) and agentic
systems work, "you risk failed tool calls, wrong parameter values, and retries
that waste context and degrade performance."

Therefore, to reduce context waste and inaccuracies, the reigning philosophy is
to design MCP tools around specific capabilities and represent them with natural
language descriptions optimized for LLMs. These could be tasks that are
business-oriented and may abstract or combine individual HTTP calls, like
`/user` or `/storefront`, and backend operations.

By providing semantically rich tooling descriptions, LLMs are better positioned
to discover and interact with them.
[One study](https://arxiv.org/abs/2602.14878) found that improving descriptions
increased median task success by 5.85 percentage points and partial goal
completion by 15.12%.

Another reason for organizing MCP tools this way is to aid determinism. LLMs can
act unpredictably when expected to chain together various methods from a large
specification on their own.

So, before generating the MCP server, consider what capabilities you want to
expose. One way to build these workflows is by using the
[Arazzo Specification](/blog/api-workflows-and-arazzo), an
OpenAPI-compatible standard for documenting API-based flows that combine
multiple endpoints.

## 2. Generate your MCP server

_Create your MCP server based on a 'source of truth' specification_

Assuming you want the MCP server to match an underlying source of truth,
generating it from an OpenAPI specification (possibly enhanced with Arazzo) is a
good idea. As I've previously covered, there are
[plenty of MCP server generators](/blog/mcp-server-generators-compared)
that can do the heavy lifting.

Some of the MCP server generators on the market enable you to select which
endpoints to expose in your MCP server, which can help avoid overexposing
sensitive endpoints. Some can also generate a semantic layer across tools with
descriptions optimized for LLM reasoning.

Alternatively, Claude Code or
[another AI assistant](/blog/bring-your-own-agent-mcp-infrastructure)
_could_ generate an MCP server. However, there are some benefits of using a
deterministic tool for this purpose: you have a more repeatable process, you can
add it to a build pipeline, and generators are usually up to date with the
[latest protocol version](https://modelcontextprotocol.io/specification/2026-07-28).

MCP servers are intended for agents, of course. But hopefully, you're generating
MCP documentation and onboarding instructions for humans along the way, too.

<CalloutDoc
  title="MCP Server Handler"
  description="Generate a remote MCP server from the routes in your OpenAPI-backed Zuplo project, and choose exactly which operations become tools."
  href="https://zuplo.com/docs/handlers/mcp-server"
  icon="book"
/>

## 3. Secure MCP access

_Secure access to MCP with authentication, OAuth, and permissions_

Poorly scoped MCP tool surfaces can not only carry a significant context cost —
they can hurt
[the security model](/blog/agentic-tool-calls-least-privilege)
too. As we've seen with recent
[Hugging Face](https://openai.com/index/hugging-face-incident-and-the-road-ahead/)
and [Replit](https://incidentdatabase.ai/cite/1152/) incidents, nondeterministic
agents can cause destructive actions when they have write access.

MCP can exacerbate these issues and also produce some novel security concerns,
underscoring the need to curate what endpoints are exposed and wrap servers in
secure access control layers.

When supplying remote MCP servers, security is especially important. You often
need to authenticate usage, validate identity, enforce account-based access
tiers, and apply rate limiting and throttling to avoid unpredictable or
malicious usage.

Arguably the easiest way to set up MCP security is via a gateway. A gateway can
enforce authentication and authorization to determine access based on the
requesting party's API key. It can behave as a universal authentication layer
over upstream MCP servers, set access policies, and throttle use.

For advanced [MCP gateways](/blog/mcp-gateway-comparison), you
can curate what tools are exposed when creating virtualized MCP server
instances, supporting a
[least-privilege approach](/blog/agentic-tool-calls-least-privilege)
to MCP access. Gateways typically
[complement preexisting identity providers](/blog/mcp-gateways-existing-auth-services)
such as Okta, Microsoft, Ping Identity, Auth0, or Curity, to enable compliant
identity flows using OAuth and OpenID Connect.

A flexible gateway, like Zuplo's
[MCP Gateway](https://zuplo.com/docs/mcp-gateway/overview), can be placed to
secure outbound and inbound MCP traffic, meaning those who are supplying MCP
servers to external developers, using MCP servers internally, or doing a
combination of both can use the same platform.

## 4. Configure and test it

_Make sure your server functions as expected_

Next comes configuration, which is relatively easy, as most agentic tools
support adding MCP servers via simple UIs or configuration files.

Then comes testing. There are a number of testing tools out there for gauging
MCP behaviors. For instance,
[MCP Inspector](https://modelcontextprotocol.io/docs/2026-07-28/tools/inspector)
is the official reference developer tool for testing and debugging MCP servers.
It ships with a web client that connects to a server and includes a sidebar for
monitoring results.

Many MCP builders also use [MCPJam](https://www.mcpjam.com/), which is helpful
in that it can test against all the major AI clients. This can be very helpful
for ensuring your MCP server authenticates easily and behaves as expected across
developer tools and agents.

## 5. Optional: monetize MCP server usage

_Consider if you should monetize your MCP server or not_

AI agents are opening up a new type of economy. In the process, another possible
step on your MCP journey is monetization. This is still an emerging area, but
[MCP monetization](/blog/mcp-monetization-models) could be done
in a few different ways.

First off, you might
[monetize the use of the server itself](/blog/monetize-an-mcp-server).
For example, perhaps your platform allows MCP server access as an add-on for a
premium subscription tier. This could be as simple as tying MCP server
accessibility to the API key of the requesting party and enforcing this access
at the gateway layer.

Alternatively, you could meter and charge the actual MCP interactions themselves
in a few different ways. For instance, you could meter and charge for all MCP
requests, or just for
[successful tool calls](/blog/charge-agents-for-mcp-tool-calls).
Again, this is something the gateway could enforce.

MCP monetization is not yet common and remains pretty untested. But if the
history and proliferation of
[API-based business models](/blog/how-to-create-business-model-around-api)
is any indication, MCP might follow a similar trajectory, making MCP server
monetization something to consider in the future.

For now, however, MCP mostly acts as another experience layer to a platform.
Many implementations can disregard this step.

## 6. Release and promote your MCP server

_Distribute your MCP server online and list in directories_

Now it's time to distribute your MCP server. If you're primarily distributing it
for internal use by colleagues, adding it to an internal service registry,
catalog, or collaborative agent platform is probably your next move.

However, if you're creating a remote MCP server intended to be used by other
consumers, you'll want to make it a bit more discoverable, with a dedicated
public Git repository.

Another way to encourage adoption is to add it to
[public MCP registries](https://nordicapis.com/7-mcp-registries-worth-checking-out/).
Adding your server to places like these could increase the chances of it being
picked up by developers and their agents.

Certain MCP registries list thousands of MCP servers. For example, MintMCP says
more than [10,000 MCP servers](https://www.mintmcp.com/servers) are available
through its platform. Other MCP registries include
[GitHub's own MCP server directory](https://github.com/mcp),
[MCP.SO](https://mcp.so/), OpenTools Registry, and MCP-Get.

However, the
[official MCP registry](https://github.com/modelcontextprotocol/registry) is
probably your best bet. You can even update the MCP server on the official MCP
registry
[via API](https://nordicapis.com/getting-started-with-the-official-mcp-registry-api/),
which might be helpful for incremental versioning, debugging, or security
enhancements.

All in all, treat it like a product, with release notes and public
documentation. Most MCP servers are only presented with a barebones README file
in a GitHub repository, lacking human-readable documentation or clear tool
descriptions and usage instructions. As Janet Wagner describes, some
[good examples of MCP documentation](https://nordicapis.com/5-examples-of-excellent-mcp-server-documentation/)
include those from Figma, GitLab, PandaDoc, SmartBear, and Webflow.

## 7. Monitor MCP interactions

_Monitor how your MCP server is actually used in production_

Once your MCP server is live, you'll want visibility into how agents are
actually interacting with it. Who is using it? Which agents are calling the
server? Which tools are used most frequently? And where are requests failing?

This is another area where a gateway can help by providing tool-level analytics,
traffic logs, and audit trails. These records can aid debugging, security
investigations, and compliance reviews while also showing which capabilities are
seeing the most real-world use.

<CalloutDoc
  title="MCP Gateway analytics"
  description="See which tools agents call, who is calling them, and where requests fail, across every MCP server behind the gateway."
  href="https://zuplo.com/docs/mcp-gateway/observability/analytics"
  icon="book"
/>

## Making the Most of MCP

MCP has seen tremendous momentum. In its
[July 2026 specification update](https://blog.modelcontextprotocol.io/posts/2026-07-28/),
the MCP team said its Tier 1 SDKs, including TypeScript and Python, were
approaching half a billion downloads per month.

But operationalizing MCP at scale is very different from simply spinning up a
server.

It requires upfront thinking about tool design and intent, along with
infrastructure to secure access, test behavior, monitor interactions, promote
discovery, and potentially monetize usage.

Following these steps should give you a more holistic approach to production
MCP, whether you're exposing servers internally or to external developers. The
end goal is to give agents useful access to your platform while keeping
interactions secure, observable, efficient, and as accurate as possible. If
you're ready to put a gateway in front of yours, the
[MCP Gateway quickstart](https://zuplo.com/docs/getting-started/mcp-gateway/portal) walks
through it.