If you're building developer tools in 2026, you're facing a new question: do you need an MCP server when AI coding assistants are already excellent at using your CLI?
It's worth taking seriously. Claude Code, Cursor, and goose can all execute shell commands, read documentation, and chain together CLI operations with impressive fluency. If your tool already has a well-designed CLI, building an MCP server might feel like duplicating work you've already done.
Here's the counterintuitive answer: you probably don't need MCP to improve your developer experience if you already have a CLI. But you might need it for everyone else that touches your product.
Your CLI Already Works
Modern coding assistants can read your CLI documentation, chain multiple commands together, handle errors and adjust their approach, parse JSON output and transform data, and remember flags and options across a session.
If your tool is developer-focused and lives in the terminal, a coding assistant can probably use it effectively right now. Deploying infrastructure, managing configurations, running tests: these workflows translate naturally to CLI commands.
The ecosystem is getting even better at this. Claude Skills allow companies to package instructions that help AI assistants use their tools correctly. Rather than hoping the assistant figures out your CLI's quirks, you can provide explicit guidance about command patterns, authentication flows, and common workflows. Expect more developer tools to ship copiable prompts and skill definitions alongside their documentation.
If you've invested heavily in a great CLI experience, that investment is paying dividends in the AI era. You don't necessarily need to rebuild those capabilities as MCP tools.
Where MCP Actually Adds Value
So when does MCP make sense? When you're serving users who would never open a terminal in the first place.
Consider these scenarios:
Account and team management. Questions like "show me our API usage for the last month" or "who has admin access to production" don't fit naturally into a developer's terminal workflow. But product managers, finance teams, and executives ask these questions constantly. They're asking them in ChatGPT or Claude, not in a terminal.
Analytics and reporting. Aggregating data across services, generating compliance reports, or reviewing billing history involves state and context that goes beyond what you'd want to pipe through shell commands. A finance lead asking "what was our spend by product line this quarter" shouldn't need to learn your CLI.
Collaborative workflows. When someone needs to "create a new API key and share it with the marketing team" or "add three new users to our staging environment," they're thinking in terms of outcomes, not commands.
Discovery and exploration. Users who aren't familiar with your tool won't know what commands exist. An MCP server surfaces capabilities through conversation, making your platform accessible to people who find CLIs intimidating or simply irrelevant to their work.
Concrete Examples
Let's make this specific. Here's where MCP adds genuine value that a CLI doesn't provide:
A payment processor: A finance team member asks Claude "what was our payment processing volume last month, broken down by region?" Rather than asking a developer to run CLI commands and aggregate results, an MCP server handles the query conversationally and formats the output for a non-technical audience.
A deployment platform: A product manager wants to know "which preview deployments are active for our mobile app?" They don't need to SSH anywhere or learn a CLI. They ask Claude, get a readable summary, and move on with their day.
A monitoring service: An operations lead needs to see "any alerts that fired in the last 24 hours for the payment service." An MCP server returns a summary of incidents with severity levels and affected services, formatted for someone who manages incidents but doesn't write monitoring queries.
A source control platform: Someone from legal asks "what open source licenses are we using across our repositories?" Instead of running license scanning tools locally, they ask Claude through an MCP integration that analyzes dependencies and generates a compliance report.
The pattern is clear: these are questions that require access to your service's data, but where a CLI creates unnecessary friction for users who aren't developers.
When to Skip MCP
Not every tool needs an MCP server. Here's when you can probably pass:
Your users are exclusively developers If everyone using your tool is comfortable in a terminal and AI assistants already work well with your CLI, MCP may not add meaningful value.
Your CLI already covers the full surface area If developers can already do everything through shell commands and there's no non-developer user base asking for access, you're solving a problem that doesn't exist.
You don't have cross-functional users If product managers, finance, legal, and operations teams don't interact with your platform, the "reach new users" argument doesn't apply.
Your resources are constrained Building and maintaining an MCP server takes engineering time. If you're choosing between improving your CLI or building MCP, and your users are developers, improve the CLI.
When MCP Is Worth the Investment
MCP makes sense when:
You have non-developer users If people in product, finance, operations, or customer success need to interact with your platform, MCP can serve them without requiring terminal literacy or requiring that they use the UI.
Your web dashboard sees heavy traffic for read-only queries Look at what people check in your UI: billing pages, usage metrics, team member lists, deployment status. These are often perfect MCP candidates because they're informational queries that translate naturally to conversation.
You want to reduce support burden If your customer support/success team fields questions like "how many API calls did we make last month" or "when does our contract renew," MCP can let users self-serve those answers.
You're building for the AI-native workflow As more teams use AI assistants as their primary interface for work, being accessible through natural language becomes a competitive advantage.
If you already have APIs for these capabilities already, MCP tools become a much lighter lift. You're exposing existing functionality through a new interface rather than building from scratch.
If you don't have APIs for account management, analytics, or administrative functions, it's worth asking why. These capabilities often live only in web dashboards because that's where non-developers were expected to access them. But if AI assistants are becoming the interface for knowledge workers, building APIs specifically to power MCP tools may be a user experience investment worth making.
Reducing the Engineering Lift
If you decide MCP is worth pursuing, the good news is you don't have to build everything from scratch. If you already have a REST API, tools like Zuplo's MCP Server Handler can transform your existing API routes into MCP tools automatically. Point it at your OpenAPI spec, configure which endpoints to expose, and you have a working MCP server without writing custom integration code.
This matters because the calculus changes when the engineering investment drops. Building MCP for non-developer use cases becomes much more attractive when you're configuring rather than coding.
A Practical Approach
The answer isn't MCP versus CLI. It's knowing where each interface serves users best.
Keep investing in your CLI for developers. Make it fast, composable, and scriptable. Consider publishing Claude Skills or prompt templates that help AI assistants use your tool correctly. This is where developers live, and where coding assistants can leverage your work most effectively.
Build MCP for the periphery: account management, analytics, reporting, billing, and administrative functions that don't belong in a developer's terminal but that other stakeholders need to access. Start with your highest-traffic dashboard pages and work backward to identify what queries would benefit from conversational access.
This approach also future-proofs your platform. As AI assistants become standard in business workflows, teams will expect to interact with your service through natural language. Meeting them there, for the right use cases, extends your reach without duplicating the work you've already done.
Your CLI isn't going anywhere. But there's a growing audience for your platform that will never use it. MCP is how you reach them.
Want to understand how teams are actually adopting MCP? Check out the State of MCP Report to see how builders and consumers are approaching MCP integration.
