What Problem Does MCP Solve for AI Agents?
When Anthropic announced the Model Context Protocol in late 2024, Mayura's founder was curious, and also unconvinced.
The founder had spent years at Microsoft as a data and AI forward deployed engineer, building software in real customer environments, and had been working with AI agents since before the industry settled on that label. GitHub Copilot and ChatGPT were already part of daily work. Years in the field teach how much an agent can accomplish when it has access to a shell, an API, a CLI, or decent documentation.
So the founder kept asking colleagues a simple question:
What problem are we having that MCP solves?
The answers were analogies. MCP could become "the USB-C of AI". Huh, okay? Standardization, discovery, interoperability. What never came back was a concrete example where an agent had been unable to use a system through existing interfaces, then became able to use it because MCP existed.
That distinction matters. A new convention can be useful without solving a new technical problem.
Agents already know how to use software
An experienced engineer does not need a bespoke integration protocol to understand an unfamiliar service. They read the documentation, inspect an OpenAPI specification, authenticate, make a request, interpret the error, and try again.
Capable AI agents already follow the same loop.
Give an agent a documented REST API and credentials. It can generate a client. Give it a CLI with useful help text. It can run commands and recover from failures. Give it a repository with examples. It can trace the intended workflow through code and documentation.
The OpenAPI specification already defines a language-agnostic interface through which humans and computers can discover and understand a service. REST, RPC, OAuth, CLIs, and software documentation already cover the execution, authorization, local access, and intent layers.
These interfaces are imperfect. So are MCP tool descriptions. Both require somebody to design a coherent contract, explain the operations, expose the right amount of context, and maintain the result.
MCP adds a client-server convention
The MCP architecture is understandable. An AI host connects to an MCP server. The server exposes tools, resources, and prompts through a standard protocol. The host lists those capabilities and makes selected ones available to the model.
That can reduce adapter work for a host that wants to support many external systems. It can also help a provider package one integration for several participating clients.
The benefit belongs primarily to the host ecosystem. Cursor, Claude, and other general-purpose AI clients can implement one convention. Tool providers can target that convention. Users may get a more consistent installation experience.
None of this removes the underlying work. The provider still needs a well-designed API or internal service. Somebody still has to describe the capabilities. Authentication, authorization, error handling, versioning, and trust remain. The customer still has to find the integration, configure it, and grant access.
MCP standardizes the adapter shape. That is a narrower claim than the hype surrounding it.
DX, CX, and AX depend on each other
The emerging pitch is that companies need a new agent experience, or AX, alongside developer experience and customer experience.
We agree that agents are becoming important software users. We are less convinced that MCP is the missing experience layer.
Good AX begins with good DX. The system needs clear operations, predictable schemas, useful errors, and documentation that explains what the software can do. An MCP server built on a confusing product contract gives the agent a standardized route to confusion.
AX also depends on CX. A customer must choose the software, trust the provider, configure the connection, authenticate, and approve what the agent can access. MCP capability discovery begins after those explicit customer steps. The protocol can enumerate tools on a server the client already knows. It does not create ambient product discovery or solve the trust decision.
By the time the DX and CX work is complete, a capable agent may already have everything it needs.
A shortcut needs a real constraint
We are launching Mayura with the same standard the founder used in customer delivery work: identify the actual constraint before adding architecture.
If users repeatedly struggle to connect their AI clients to a product, an MCP adapter may earn its place. If a provider is maintaining separate integrations for several hosts, a shared convention may reduce that burden. Those are testable conditions.
"Agents will use software" is not enough. Agents already use software.
Our concern is that MCP is becoming an answer before the industry has named the question. It packages existing capabilities for a particular class of AI host, then invites every software company to treat the package as a new foundation.
For now, we will keep starting with the API, CLI, and documentation. We will test them with the agents people actually use. If evidence shows that an MCP server removes recurring work, we will add one.
Until then, the question stands: what problem are we having that MCP solves?
Mayura is Continuous Intelligence: it reads the feeds, inboxes, and documents a team checks by hand today and delivers each morning's answers already assembled, on edge AI infrastructure the company owns. If you want to work that through against your own workflows, start with our services.