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The Model Context Protocol seeks to bring a standards-based and open source approach to enterprise ...
More use of LLMs and agentic AI. The Model Context Protocol was released in late 2024, but over the past two weeks it has caught fire with the developer community. And rightly so, since — with the exception of RAG — the AI space has sorely lacked the standards necessary to drive widespread adoption of agents and agentic workflows.
Here’s a quick breakdown of MCP and why you should care about it. MCP is an open source protocol released by Anthropic that is meant to standardize how applications can provide context to LLMs. This is a very important idea since the status quo we are dealing with now is kind of a mess for developers looking to build agents.
For example, let’s say you are writing an agent that connects to an LLM (for instance Anthropic’s Claude, but MCPs are LLM-agnostic). In MCP terms, that is called a client. These days, every service or application that wants to work with a client — which could also be a developer tool or a chatbot — uses APIs to interact.
But the challenge is that all of these APIs are different, and if you start wanting to connect your client to multiple services or functions across multiple providers, it gets super complex very quickly. Worse, it’s fragile because APIs can and often do change. Enter MCP, which allows services or applications to wrap and serve their APIs to an MCP client.
This greatly simplifies the development of agents because there is now a standard way for the client (and in turn an LLM) to work with the server. The server in this case can do a lot of things. For example, it could be a database, a git repository or even something like Google Maps.
This variety of servers also makes the client application far more powerful because it enables the use of more complex agents that chain together multiple tasks. It’s important to state that while MCP is very promising, it is an emerging standard. As of now, MCP is primarily being promoted in the professional developer space, which is a great place for standards to be tested and evolved.
But looking forward, MCP has the potential to benefit almost every stakeholder in the world of AI. Let’s take a quick tour of the potential beneficiaries. Startups — AI startups are already getting on board quickly with MCP.
The rationale is that many startups are building AI products that do one thing really well. MCP enables best-of-breed tools to be easily inserted into enterprise apps. In many ways, this is a new route to market for AI startups, somewhat like the Android Play store was for fitness devices.
Pro-code developers — In its current state, the primary user for MCP is the pro developer, who benefits from faster and better agent creation. Unsurprisingly, we are already seeing MCP clients being embedded into Anthropic’s Claude Code ( which I wrote about here ) and Microsoft’s GitHub Copilot. However, there are some more advanced MCP features such as roots and samples, which also provide some enterprise governance capability.
No-code and low-code developers — I expect to see a fairly rapid trickle-down effect in which no-code tools such as Google Agentspaces ( which I wrote about recently ) or Microsoft Power Apps become MCP clients. This will enable less-technical business users to more easily integrate an agent running the MCP client code with existing apps and services such as Slack, Google Drive, Sharepoint, relational databases or SaaS tools. SaaS providers — SaaS is big on agents, and I have recently covered both ServiceNow ’s and Salesforce ’s strategies in this vein.
The easiest example use case in the current context is that MCP mitigates the need for these providers to maintain their own proprietary integration hubs. However, there is also a huge possible benefit to each SaaS provider by becoming an MCP server and enabling third-party agents to work with the existing datasets stored in the SaaS. This could enable new ecosystem extensions and other expansion opportunities.
AI frameworks and hyperscalers — I am lumping these two together since the hyperscalers do tend to have their own AI development frameworks (which I recently covered here and here ), and hyperscalers have multiple MCP opportunities. First, as a server, a hyperscaler can open up services to agents. For instance, AWS already has a couple of open source MCP servers in operation.
However, hyperscalers also have their own models and tools, which could present a great opportunity to create or deploy new MCP clients. AIs deployed outside the cloud — As we see more MCP servers being deployed, we may also see an opportunity for more local AI deployments near localized data. In the roadmap for MCP , we see a plan for more remote services including OAuth and stateless operation.
Could we see a future where hybrid agents can coordinate between locally and remotely deployed AIs? If that is the case, we may see more momentum to deploy small models on local infrastructure to protect sensitive or legacy data. Again, this is a very new protocol, and although it is gaining momentum, there are still a couple of notable hurdles ahead. Configuration and programming are still a challenge.
These things aren’t impossible to achieve, but there will be a need for easier deployment and fuller documentation as the standard takes hold. Single-vendor “standards” aren’t actual standards . Anthropic has done us all a favor by developing MCP and releasing it as open source.
But we still need to see some big names get behind it to give the market confidence that it’s here to stay. Since MCP is LLM-agnostic, that big player does not have to be a model builder per se (although tacit approval from Google or OpenAI would be nice). But gaining support from a big player with AI tools (maybe AWS) and/or a number of possible MCP servers (such as Oracle or other SaaS players) will show the market that MCP is viable and that customers can move forward.
MCP shows a lot of potential, and standards are generally the pathway to mainstream adoption. While we can assume the open source community will smooth out the ease of use and documentation issues, the main challenge in going mainstream is acceptance and participation from other vendors. Looking at the calendar, it jumps out that there are a lot of vendor events coming up where that kind of practical support could be expressed.
I expect we will know a lot more about the kind of traction MCP is getting in the next 60 days. Moor Insights & Strategy provides or has provided paid services to technology companies, like all tech industry research and analyst firms. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking and video and speaking sponsorships.
Of the companies mentioned in this article, Moor Insights & Strategy currently has (or has had) a paid business relationship with AWS, Google, Microsoft, Oracle, Salesforce and ServiceNow..
