How Model Context Protocol is shaping the future of AI and search marketing

LLMs and AI tools have transformed nearly every industry, including marketing. 

We’ve become accustomed to AI’s ability to:

• Generate text, images, and video.
• Summarize articles.
• Transcribe audio.
• Write code.
• Access webpages.

But as these models evolve, their capabilities are entering a new phase with the introduction of Model Context Protocol (MCP) – a development that will also reshape how we think about search visibility.

MCP allows LLMs and AI systems to connect more easily to external data sources and tools, giving organizations a new way to deliver meaningful content to both AI systems and their users.

What is Model Context Protocol?

Model Context Protocol (MCP) is an open protocol framework that allows AI systems to connect directly to a data server, standardizing how information provides context to LLMs.

It also enables developers to build tools and applications that integrate with LLMs, allowing them to access external data and workflows through the integration.

Here’s an analogy to understand how this works:

Pretend LLMs are like librarians at your local library. They know every book in their local database and know how to search and find information within it. 

The limitation here is that the librarian only has working knowledge of this local library and cannot access any books or information outside of this library.

This means that if you’re a library visitor researching a topic, the librarian can only offer you information available within the books in the local library’s database, which may include outdated information if the library only has books from 2015.

However, with MCP, the librarian (LLM) is given the resources to instantly access any book in the world and can provide up-to-date information on a topic, straight from a primary source. 

MCP allows LLMs to:

• Easily access data and tools directly from a source.
• Get instantaneous, up-to-date information from a server, so they no longer rely only on pretrained knowledge.
• Leverage agentic capabilities, such as implementing automated workflows and search databases.
• Perform actions by connecting to custom tools created by third parties, developers, or organizations.
• Provide exact citations for information sources.
• Extend past data retrieval and into capabilities such as integrating with shopping APIs, allowing LLMs to purchase items directly.

In a real-world example for an ecommerce business, this could look like an LLM: 

• Having secure access to an internal inventory system to pull real-time data, such as product pricing.
• Providing a bulleted list of product specs directly from your inventory database. 

LLMs could not only market directly to a user searching for the season’s latest running shoes but could also purchase a pair of shoes for the user.

MCP vs. RAG

MCP may sound similar to retrieval-augmented generation (RAG) in how LLMs can gather dynamic and up-to-date information beyond their static pretraining. 

Still, they vastly differ in how LLMs fundamentally access and interact with information. 

How RAG works

RAG enables an LLM to retrieve information in a series of steps:

• Indexing: The LLM converts external data into a vector embedding database that is then utilized during a retrieval process.
• Vectorization: Submitted search queries are also converted into a vector embedding
• Retrieval process: A retriever then searches its vector database to fetch the most relevant information based on how similar the query’s vector embeddings are to those in its existing database.
• Providing context: Once the information is retrieved, it is combined with the search query to provide additional context through a prompt.
• Output generation: The LLM will then generate an output based on the retrieved information and its own training knowledge.  

How MCP works

On the other hand, MCP essentially functions like a USB port for AI systems, standardizing how data is connected to the LLM. 

Unlike RAG, MCP follows a client-server architecture and is much more comprehensive and seamless in the way it accesses information by using the following process:

• Client-server connection: LLM applications are hosts that initiate connections. Through the host application, clients can have 1:1 connections with data servers, which provide the tools and context to the clients. 
• Tools: Developers can create MCP-compatible tools, utilizing the open protocol to execute functions such as API calls or access external databases that allow LLMs to perform specific tasks.
• User requests: Users can make specific requests, such as “What is the price of the newest Nike running shoe?”
• AI system request: If the AI system or LLM is connected to a tool with a Nike-created inventory pricing database, it can request the price of the newest shoe.
• Output with live data: The connected database can deliver the live data to the LLM and provide up-to-date live data directly from Nike’s database.

	RAG	MCP
Architecture	Retrieval system	Client-server relationship
How data is accessed	Retrieval through vector database	Connecting with custom tools created by parties
Output capabilities	Relevant information retrieved from database.	Customized outputs and functions, including agentic capabilities, based on tools.
Data recency	Dependent on when content was last indexed.	Up-to-date from the live data source.
Data requirements	Must be vector encoded and indexed.	Must be MCP compatible.
Information accuracy	Reduced hallucinations through retrieved documents.	Reduced hallucinations through access to live data from a source.
Tool use and automated actions	Not possible.	Can integrate with any tool flow provided on the server and perform any provided action.
Scalability	Dependent on indexing and window limits.	Can scale up easily depending on MCP-compatible tools.
Branding consistency	Inconsistent since data is pulled from various sources.	Consistent and strong, since brand-approved data can be pulled directly from the source.

Dig deeper: The next wave of search: AI Mode, deep research and beyond

What this means for search marketers and publishers

Although Anthropic was the first to introduce the concept of MCP in November, many companies, including Google, OpenAI, and Microsoft, are planning to adopt Anthropic’s MCP concept in their AI systems. 

This means that search marketers should focus on increasing content visibility through MCP tools and consider the following:

Work with developers for integration

Collaborate with developers to consider how to serve high-value content to users while providing meaningful context to LLMs through MCP-compatible tools. 

Consider how to take advantage of agentic capabilities executed through the MCP framework.

Implement structured data

Structured data and schema will continue to provide reliable reference points for LLMs. 

Use them to support machine-readability for content served through custom tools. 

This also improves visibility within AI-generated search experiences, ensuring that content is understood and surfaced accurately.

Keep information up-to-date and accurate

Since LLMs connect with data sources directly, confirm that all content provides relevant, up-to-date, and accurate data to support trustworthiness and a good user experience. 

For an ecommerce company, this would include verifying price points, product specs, shipping information, and other essential details, especially as this data may be delivered directly in AI search responses.

Emphasize brand voice and consistency

One clear advantage of customizing tools for MCP is the ability to establish a strong and consistent brand voice for LLMs. 

Rather than relying on fragmented information from various sources, MCP-compatible tools let you maintain a consistent brand voice by delivering authoritative content directly to LLMs.

Integrate MCP tools into your marketing

As AI systems adapt to MCP, forward-looking marketers should include this new framework within their strategies and collaborate cross-functionally to develop tools that can serve high-value content to LLMs and effectively reach users. 

These tools won’t just support automation – they may also become core to how brands appear in AI-driven search.

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