Most “AI-ready” products add a chatbot to an existing dashboard and call it innovation.
The chatbot can answer questions, summarize information, and perhaps trigger a few predefined actions. But the product itself remains unchanged. Users still need to open the application, learn its navigation, move between modules, complete forms, and understand where every feature lives.
MCP-first software represents a much more fundamental shift.
The next generation of applications has two users: the human and the AI agent. The human provides intent, judgment, and approval. The agent discovers the relevant capabilities, gathers context, and executes the work.
This changes how software should be designed, how its capabilities should be exposed, and even how important its traditional interface will remain.
MCP, or Model Context Protocol, provides a standard way for AI systems to connect to external tools and data. Instead of building a different integration for every AI platform, an application can expose its capabilities through a shared protocol that MCP-compatible clients can understand.
But adding an MCP server to an existing application does not automatically make it MCP-first.
MCP-first means designing the product around a new question:
If an AI assistant had to complete this job with the user, what context, instructions, permissions, and tools would it need?
It also requires a second, equally important question:
What should the agent never be allowed to access, change, or execute without approval?
An MCP-first application is not simply software with an AI feature. It is software whose core capabilities are structured so that humans and agents can discover, combine, and operate them safely.
The UI is no longer where most of the work happens
In an MCP-first application, the traditional user interface becomes less important.
It does not disappear. Its role changes.
Users will still open the application to manage operational matters. They will configure workspaces, connect data sources, manage permissions, review billing, monitor usage, approve sensitive actions, inspect detailed reports, and handle exceptional cases.
However, the heaviest and most frequent usage will increasingly happen outside the application itself.
It will happen inside Claude, ChatGPT, Cursor, or any other MCP-supporting AI client.
Instead of opening an application and navigating through five different modules, users will describe the outcome they want.
A marketing manager might ask:
Create a dynamic email template for our upcoming webinar. Use our existing brand system, personalize the content by audience segment, and connect the registration button to the correct campaign.
An account manager might ask:
Which of my tasks need attention today, what priorities are falling behind, and which opportunities have not progressed during the last seven days?
A growth lead might request:
Compare this month’s campaign performance with the previous three months, identify the largest drop-offs, and create three experiments for the next sprint.
The user does not need to know which feature, table, or module contains each piece of information. The AI client identifies the appropriate tools, retrieves the necessary context, combines the results, and returns an answer or completes the requested action.
This is a major departure from traditional SaaS.
For years, software companies competed through interface design. The better product was often the one with clearer navigation, fewer clicks, and more intuitive dashboards. Those things still matter, but they are no longer the complete product experience.
The quality of an MCP-first application is also determined by how well an agent can understand and use it.
Can the agent discover the correct capability?
Can it understand the required inputs?
Can it distinguish between reading and writing?
Can it combine several tools to complete a larger task?
Can it explain what it intends to change before acting?
Can the application prevent it from accessing the wrong workspace or consuming an unlimited budget?
The application UI becomes the operational control plane. MCP becomes the primary usage layer.
In traditional SaaS, the UI is the product and the API supports it. In MCP-first software, the UI governs the system while MCP allows users and agents to operate it from wherever the work is already happening.
From navigating features to describing outcomes
Traditional applications require users to understand how the software is organized.
To create a campaign, the user must find the campaign module. To check performance, they must open analytics. To update an opportunity, they must navigate to the CRM. To create an email, they must find the email builder.
The structure of the software determines the journey.
In an MCP-first environment, the user begins with the desired outcome. The agent determines which capabilities are needed to reach it.
This allows software capabilities to be combined dynamically rather than being permanently encoded into screens and menus.
For example, “Prepare next week’s webinar” might require the agent to:
- Retrieve the webinar details.
- Check the speakers and their availability.
- create the registration page.
- Generate the promotional email.
- Prepare social media content.
- Add the related tasks to the current sprint.
- Check whether the tracking and CRM fields are configured correctly.
In a traditional application, this could require several modules and multiple team members. In an MCP-first application, the user expresses one objective and the agent coordinates the necessary tools.
The experience shifts from operating software to directing work.
The three signals of an MCP-first product
Although much of the usage moves into AI clients, the application interface still has an important role. It must help users create, package, and expose capabilities in ways that agents can understand.
Three actions are becoming particularly important.
Generate with AI
“Generate with AI” accelerates work inside the system of record.
It might create an email, campaign structure, product description, report, landing page, workflow, or first draft. The important point is not simply that an LLM generated some content. The output is created inside the application where the relevant brand data, permissions, templates, and workflows already exist.
Generation is not the final product. It is the starting point that keeps the work connected to the systems where it will be reviewed, approved, distributed, and measured.
Export skill
A skill packages how a particular job should be performed.
It can explain what a module does, which tools and files are relevant, what sequence should be followed, what standards must be respected, and what an agent should never do.
This makes operational knowledge portable.
Instead of sending a colleague or AI assistant a screenshot and a collection of disconnected instructions, the application provides a structured playbook that Claude, Cursor, ChatGPT, or an internal agent can follow.
MCP exposes what the application can do. Skills explain how those capabilities should be used to complete meaningful work.
Without skills, MCP can become little more than another collection of APIs. Skills turn those APIs into repeatable processes.
Ready for LLMs
“Ready for LLMs” provides the information needed for an AI assistant or AI-assisted developer to implement something correctly.
This might include an embed, webhook, API connection, tracking setup, component, or integration. The application should provide the relevant instructions, configuration, context, and constraints in a form an LLM can understand.
It is no longer enough to place generic documentation in a help center and expect every user to interpret it correctly.
MCP-first products need a machine-readable onboarding and implementation path.
These three actions serve different purposes:
- Generate with AI helps create.
- Export skill packages expertise.
- Ready for LLMs makes implementation and integration understandable to machines.
Together, they connect the application UI with the wider agent ecosystem.
MCP is not a button. It is infrastructure.
These interface signals are useful only when supported by serious infrastructure.
An MCP-first application needs the same discipline that would normally be applied to a production API platform. AI agents are automated clients capable of making repeated decisions and calls. If they are given broad access without proper controls, even a simple misunderstanding can become expensive or dangerous.
The real work of becoming MCP-first happens underneath the interface.
Authentication: who is the agent acting for?
An AI agent must never be allowed to wander across an application’s entire database.
Every request must be connected to a specific user, brand, workspace, role, and set of permissions.
A production MCP implementation may need standard OAuth, including PKCE for desktop and development clients, along with clear discovery and consent flows. It may also need API keys for server-to-server automations that should not depend on an interactive browser session.
In a multi-tenant platform, authentication must carry the correct tenant context. A token should not simply confirm that someone has access to the platform. It should determine which workspace the agent is operating in and what it may do there.
Client registries are also important. Third-party applications, redirect addresses, and integrations should be explicitly recognized rather than accepted accidentally.
If authentication does not map cleanly to the product’s tenancy and permission model, MCP support can quickly become a security risk.
Permissions must reflect the consequences of an action
Not every tool call carries the same level of risk.
Reading a list of tasks is different from deleting a task. Drafting an email is different from sending it. Preparing an opportunity update is different from changing the value of the deal. Generating a campaign is different from publishing it.
MCP-first applications should distinguish between actions that can run automatically and actions that require explicit approval.
This distinction should be visible both to the agent and to the user.
An agent should be able to say:
I have prepared the email and selected the audience. Sending it will reach 12,430 contacts. Would you like me to proceed?
That is a much safer model than giving the agent unlimited permission and expecting it to infer when confirmation is necessary.
Cost measurement: what did the agent consume?
If every tool call is free and invisible, agents can explore until the platform receives the bill.
An MCP-first product needs to measure usage accurately. In many cases, this means separating at least two types of cost.
The first is platform or API usage. This includes tool calls, workflows, data retrieval, messages, and other platform operations.
The second is AI usage. This includes any downstream model costs incurred when the platform itself calls an LLM, generates media, performs research, or runs another AI service.
These costs should not be treated as identical.
Listing available tools should not cost the same as running a complex research workflow. Reading one task should not have the same weight as producing a complete video campaign.
A weighted metering system allows the platform to keep discovery inexpensive while pricing expensive actions according to the resources they consume.
Without this separation, customers cannot understand their usage and the platform cannot operate sustainably.
Quotas and rate limits
Agents are persistent. That is one of their advantages, but it also means they can produce far more requests than a human user.
MCP-first applications need monthly usage budgets, plan-based quotas, and per-client rate limits. They also need clear responses when those limits are reached.
A well-designed system should explain whether the request failed because of a temporary rate limit, an exhausted monthly quota, insufficient permissions, or a required plan upgrade.
Connection and discovery operations such as initialization, listing tools, and health checks should normally remain free or non-billable. Clients should be able to connect and understand what is available without consuming the user’s operational budget.
Quotas are not simply restrictions. They protect both the platform and the customer from unexpected usage.
Tools: the right capabilities, not every capability
An MCP server with two simple tools may be useful for a demonstration, but it will not support substantial work.
At the other extreme, exposing thousands of unstructured tools creates a different problem. The agent may struggle to identify the correct capability, confuse similar tools, or select an action with unintended consequences.
The objective is not to expose the largest possible number of tools. It is to make the appropriate tools discoverable, understandable, affordable, and safe.
A mature MCP-first application needs a tool registry through which capabilities can be enabled or disabled by tenant. When the catalog becomes large, the application may need search-and-invoke patterns rather than sending the full list to every client.
It should also provide curated bundles. A sales assistant may need CRM, opportunity, task, and communication tools, but not infrastructure management. A content assistant may need articles, media, brand assets, and publishing tools, but not financial administration.
Tools should be grouped around jobs, roles, and operational contexts.
An MCP-first platform can also federate capabilities from other MCP servers. Voice services, data providers, analytics systems, or specialist applications can sit behind a shared authentication, metering, and governance layer.
New capabilities can also be exposed through functions or lightweight services without waiting for a large monolithic application release.
The meaningful metric is not the total number of tools. It is how many tools an agent can correctly discover, combine, and use without becoming confused or creating business risk.
Observability: if you cannot see it, you cannot trust it
Agent activity must be observable.
Product teams need to understand which client connected, which user authorized it, which tools were discovered, what calls were made, which actions succeeded, how long they took, and what they cost.
This is necessary for debugging, billing, security, and customer support.
It is also important for trust. Users should be able to inspect what an agent has done and understand why a particular change occurred.
Monitoring is not an operational afterthought. It is part of the MCP product experience.
Old SaaS versus MCP-first software
| Old SaaS | MCP-first application |
| The application UI is the primary destination | The AI client becomes the primary workspace |
| Users navigate features and modules | Users describe outcomes and agents select the tools |
| The UI handles everyday execution | The UI focuses on configuration, governance, review, and exceptions |
| Workflows must be designed as screens | Capabilities can be combined dynamically through conversation |
| Features are designed mainly for clicks | Capabilities are designed for humans and agents |
| Documentation is an afterthought | “Ready for LLMs” is a first-class product action |
| Operational knowledge remains tribal | Knowledge can be packaged as exportable skills |
| AI is added as a sidebar | AI operates across the workflow |
| Integrations rely on brittle custom connections | Tools share authentication, metering, quotas, and common conventions |
| API keys are passed around informally | OAuth, consent, client registries, and tenant-scoped access are built in |
| The API supports the interface | The interface governs the system while MCP operates it |
Why this matters for product and growth teams
The bottleneck for most teams is not the absence of another dashboard.
The bottleneck is transferring intent into execution.
An idea must become a brief. The brief must become a campaign. The campaign must be translated into tasks. Those tasks must be distributed across people and systems. The results must then be collected, interpreted, and converted into the next experiment.
Every handoff creates delay and loses context.
MCP-first design compresses these transfers.
Generation happens inside the object being created. Skills allow the next person or agent to inherit the correct method. LLM-ready instructions reduce the friction of implementation. Auth, approvals, metering, and quotas allow agents to work without giving them uncontrolled access.
Humans continue to provide direction, taste, judgment, and accountability. Agents stop starting from zero every time work moves from one system to another.
This is particularly important for growth teams because their work naturally crosses multiple functions. A single campaign might involve research, content, design, development, CRM, paid media, analytics, sales, and community operations.
Traditional software divides those activities into separate applications.
MCP-first software gives the agent a way to coordinate them around the intended outcome.
How GrowthOS approaches MCP-first software
GrowthOS is our MCP-first growth operating system. It brings together brands, communities, learning programs, campaigns, CRM, content, media, embeds, and operational workflows so that teams and agents can work from the same underlying system.
Its traditional interface remains important, but it is increasingly becoming the place where users configure, govern, review, and monitor the system.
The everyday work can happen through Claude, ChatGPT, Cursor, or another MCP-compatible environment.
A user can ask for their priorities, review task progress, inspect opportunities, create a dynamic email, prepare a campaign, produce a performance summary, or coordinate the next experiment without manually navigating through every GrowthOS module involved.
Across the product, three actions support this model: Generate with AI, Export skill, and Ready for LLMs.
Underneath those actions is a hosted MCP layer with OAuth and API-key clients, brand-scoped access, weighted usage metering, separate AI-credit consumption, plan quotas, rate limits, an enableable tool registry, curated bundles, upstream MCP federation, and monitoring of real sessions and executions.
This infrastructure is what allows AI agents to do useful work without receiving unlimited access to the application.
We are not adding AI on top of traditional growth software.
We are rebuilding growth software so AI can operate inside it safely, with context and through a common protocol.
The future application will still have a user interface. But that interface will no longer be the only place, or even the main place, where users experience the product.
The UI will manage the system.
The agent will operate it.
That is what MCP-first means.
What is an MCP-first application?
An MCP-first application is software designed so both humans and AI agents can use its core capabilities. Instead of treating AI as a chatbot added to an existing interface, the product exposes its tools, context, permissions, and workflows so agents can discover and combine them to complete real work.
What is the difference between MCP support and an MCP-first product?
Adding an MCP server does not make a product MCP-first. MCP support exposes capabilities to AI clients. MCP-first design goes further: authentication, permissions, tools, documentation, metering, approvals, and workflows are designed from the beginning around humans and agents working together.
Does MCP-first mean applications will no longer need a UI?
No. The UI does not disappear, but its job changes. It becomes the control plane for configuration, permissions, billing, approvals, monitoring, detailed reporting, and exceptions. More of the everyday execution can happen through AI clients such as ChatGPT, Claude, Cursor, or other MCP-compatible environments.
How is MCP-first software different from traditional SaaS?
Traditional SaaS asks users to navigate features, screens, and modules. MCP-first software lets users describe the outcome they want and allows an agent to determine which capabilities are required. The experience moves from operating software to directing work.
What is the role of MCP in an MCP-first application?
MCP provides the common protocol through which AI clients can discover and use an application’s tools and data. Instead of creating a different integration for every AI platform, the application can expose capabilities through a shared layer that MCP-compatible clients understand.
What are the three signals of an MCP-first product?
Three useful product signals are Generate with AI, Export skill, and Ready for LLMs. Generate with AI creates work inside the system of record. Export skill packages the knowledge required to perform a job correctly. Ready for LLMs provides machine-understandable instructions for implementation and integration.
What is the difference between an MCP tool and a skill?
A tool describes what an application can do. A skill explains how those capabilities should be used to complete a meaningful job. MCP exposes capabilities; skills package the operational knowledge, sequence, standards, and constraints needed to combine them correctly.
How do you make MCP access safe for AI agents?
Start with scoped authentication, tenant-aware permissions, explicit approval for consequential actions, quotas, rate limits, usage metering, and observability. An agent should not simply have access to an application. It should have access to the right workspace, tools, and actions for the user it represents.
Why do MCP-first applications need usage metering and quotas?
Agents can make far more requests than human users and can trigger expensive downstream operations. Metering shows what was consumed, while quotas and rate limits prevent unexpected costs or runaway usage. Discovery should remain inexpensive while resource-heavy actions can be weighted according to their actual cost.
Should an MCP server expose every feature in the application?
No. More tools do not automatically create a better agent experience. A useful MCP implementation exposes capabilities that agents can reliably discover, understand, combine, and use safely. Large catalogs may require tool search, curated bundles, tenant-level controls, and role-specific capability sets.
Why does MCP-first software matter for growth teams?
Growth work naturally crosses research, content, design, CRM, development, paid media, analytics, sales, and operations. MCP-first software allows an agent to coordinate capabilities across those functions around one objective, reducing the handoffs and context loss that slow traditional workflows.
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Theodore has 20 years of experience running successful and profitable software products. In his free time, he coaches and consults startups. His career includes managerial posts for companies in the UK and abroad, and he has significant skills in intrapreneurship and entrepreneurship.
