What is Model Context Protocol?

While both RAG and MCP deliver context to GenAI apps, they can be seen as complementary or potentially overlapping approaches to enhancing the accuracy and security of LLM reponses within the context of enterprise data. Let’s take a closer look at their relationship: 

Retrieval-Augmented Generation (RAG) 

• Objective 

  RAG grounds LLMs on specific, up-to-date knowledge from external data sources at inference time.

• Procedure 

  RAG retrieves relevant structured and unstructured data from enterprise systems and knowledge bases whenever a user poses a query. The context derived from the data is then combined with the user's prompt and fed into the LLM to generate a more informed response.    

• Focus 

  RAG provides the LLM with the necessary context to answer a specific query accurately, reducing AI hallucinations and improving relevance.    

Model Context Protocol (MCP) 

• Objective 

  MCP establishes a secure, standardized, and efficient two-way connection between GenAI apps (MCP clients) and enterprise data sources (accessed via MCP servers). 

• Procedure 

  MCP defines a protocol for its clients (GenAI apps) to request and receive data from its servers (data sources). The servers manage authentication, authorization, data retrieval from various backends, data masking, and potentially even expose specific tools and functions.    

• Focus 

  MCP enables GenAI apps to access and interact with enterprise data in a governed and streamlined manner, supporting various use cases, beyond answering questions.   

How RAG and MCP relate 

1. Data access 

   Both RAG and MCP address the challenge of providing GenAI apps with access to relevant data. RAG for structured data retrieves information based on the semantic similarity of a query, while MCP controls the request for, and exchange of, data between client and server.    

2. Contextual information 

   Both RAG and MCP provide LLMs with the context. RAG explicitly injects retrieved context into the prompt. MCP provides context through the data retrieved from MCP servers, which can be used to influence the LLM's response or actions.    

3. Hallucination prevention 

   By grounding the LLM on trusted enterprise data, both RAG and MCP can help reduce generative AI hallucinations. RAG does this by providing specific data, while MCP ensures the model is drawing from authorized data sources.    

4. Real-time data 

   RAG works with real-time enterprise data sources. MCP provides consistent access to enterprise data via real-time streaming.    

5. Security and governance 

   RAG needs to implement security measures for accessing and handling retrieved data. MCP comes with built-in security and privacy LLM guardrails in the form of authentication, authorization, and data masking management. MCP offers a more centralized approach to data governance in the context of GenAI access.    

6. Use cases 

   RAG is heavily used for question answering, chatbots, and general data retrieval. MCP supports a broader range of use cases, including agentic AI models that need to decide on, and then take, actions.    

In theory, RAG could be implemented in an MCP infrastructure. An AI app (MCP client) could use the protocol to query an MCP server for relevant data. The MCP server, in turn, could orchestrate data retrieval from various sources, potentially including enterprise systems and knowledge bases used for RAG-style retrieval. The retrieved information could then be used as context for the LLM's generation.   

MCP and RAG are not mutually exclusive. MCP can provide the secure and governed data access layer that RAG can then leverage to retrieve specific context for its generation process. MCP offers a broader framework for AI-data interaction, while RAG is a specific technique focused on improving the quality of generated text based on retrieved information.   

The Model Context Protocol (MCP) architecture was specifically designed to enable standardized communication between LLMs and a diverse range of integrations. This section outlines the fundamental components allowing MCP to unify data access for GenAI workflows.3 

Overview 

MCP follows a client-server architecture where: 

1. Hosts  are LLM apps that initiate connections 

2. Clients  maintain direct connections with servers, inside the host app 

3. Servers provide context, tools, and prompts to clients 

Core components 

1. Protocol layer
   The protocol layer handles message framing, request/response linking, and high-level communication patterns.

2. Transport layer
   The transport layer handles the actual communication between clients and servers. MCP supports multiple transport mechanisms, including Stdio transport for local processes and HTTP with Server-Sent Events (SSE) for server-to-client messages and POST for client-to-server messages.

3. Message types
   MCP supports the following message types:

– Requests expect a response from the other side.
– Results are successful responses to requests.
– Errors indicate that a request failed.
– Notifications are one-way messages that don’t expect a response. 

Connection lifecycle 

1. Initialization

– Client sends an initialize  request with protocol version and capabilities
– Server responds with its protocol version and capabilities
– Client sends an initialized  notification in acknowledgment
– Client and server begin a normal message exchange 

2. Message exchange 

After initialization, the following patterns are supported:

– Request-Response, where the client or server sends requests and receives a response
– Notifications, where either party (client or server) sends one-way messages 

3. Termination 

Either party can terminate the connection via: 

– Clean shutdown via close ()
– Transport disconnect
– Error conditions 

4. Error handling 

MCP defines the following standard error codes:

– Parse error = 32700
– Invalid request = 32600
– Method not found = 32601
– Invalid parameters = 32602
– Internal error = 32603 

SDKs and apps can define their own error codes above 32000.

Error alerts are distributed through: 

– Error responses to requests
– Error events on transports
– Protocol-level error handlers 

At a high level, MCP is an open protocol operable in any architecture – whether on-prem, cloud, or hybrid – providing a discoverable, composable, and open interface used by all major GenAI tools.

While the benefits of MCP can be significant, it also has its security risks4: 

1.   Stolen tokens and compromised accounts  

MCP storage of Open Authorization (OAuth) tokens is a critical vulnerability. For example, if unauthorized users get access to your Gmail token, they’d be able to:

– Access your entire email history 
– Send, forward, and delete messages from your account 
– Identify and use your Personally Identifiable Information (PII) 

2.   Compromised MCP Servers 

MCP servers represent a particularly attractive target for malicious actors due to their role in centralizing OAuth tokens for numerous services. Attackers could: 

– Access all your tokens, including those for Gmail, Google Drive, Calendar, and more.
– Take unauthorized actions, across all these interconnected platforms.
– Get at corporate resources, if you linked your work accounts through the MCP server.
– Persist even after you change your password, because OAuth tokens often maintain their validity independently. 

3.   Indirect prompt injection threats 

MCP introduces a new threat through indirect prompt injection. Since the AI assistant interprets natural language commands before sending them on to the MCP server, attackers could craft seemingly benign messages containing concealed malicious instructions. 

For instance, an email that appears harmless could contain embedded text that, when processed by the GenAI app, instructs it to forward all financial documents to an external address. This subtle threat is particularly dangerous, as users may be unaware that sharing certain content with their AI could lead to automated and harmful actions being performed through MCP, blurring traditional security boundaries between content viewing and action execution. 

4.   Lax and aggregated permissions 

To provide the broadest possible functionality, MCP servers often request extensive permissions, introducing significant privacy and security concerns, such as the following: 

– MCP servers may be granted unnecessarily wide-ranging access to connected services (e.g., full access to your email account instead of more restrictive read-only permissions). 

– Centralized storage of authentication tokens might lead to data aggregation in the MCP server. 

– Malicious actors who manage to gain access to the server could conduct correlation attacks across interconnected services. For example, with access to both your calendar and email accounts, attackers could mastermind highly targeted phishing or extortion campaigns. 

– Legitimate server operators could, in theory, mine aggregated user data across services for commercial gain or to build detailed user profiles. 

Additionally, while most apps were originally designed to provide segregated access to user data, the concentration of access to different services within a single protocol may seriously alter established security guardrails. 

Until MCP came along, connecting LLMs to external data had dev teams creating separate integrations for every API and database – each with different authorizations, data formats, and error handling. By standardizing these interactions MCP delivers5: 

1.   Quick integrations 

With MCP, you can plug-‘n-play new capabilities without having to custom-code each from scratch. If there’s an MCP server for a database, then any MCP-compatible LLM can connect to it. The protocol enables LLM funtion calling for retrieving data, querying databases, or recruiting APIs, as needed, just by adding the right server. Imagine a library of pre-made plugins that make specific capabilities available through one standardized protocol. 

2.   Autonomous agents 

MCP let LLM-powered autonomous agents make decisions and perform tasks without human intervention. Autonomous agents use MCP to enhance LLM capabilities by integrating with various tools, accessing APIs, retrieving information, and managing workflows in real time. And with memory and reasoning components, they can suggest strategies, learn from past interactions, and continuously improve performance. MCP helps autonomous agents develop not only the thinking – but also the action-taking – of GenAI by giving it standardized access to all relevant data. 

3.   Easy setup 

Because MCP is a universal interface, developers no longer need to maintain separate integrations. Once an application supports MCP, it can connect to any number of services through a single mechanism – reducing the manual setups required each time you want your LLM to use a new API. Dev teams can focus on higher-level logic rather than rewriting connection code for the umteenth time.  

4.   Universal language 

MCP standardizes a universal request-response language across tools so your LLM needn’t cope with one response for one service, and another response for another. All function calls and tool results are communicated in a uniform structure, for simpler debugging and scaling. MCP also ensures your integration logic is future-ready – even if you switch vendors, MCP’s interface to the tools remains the same. 

5.   Conversational context 

MCP maintains context in the never-ending conversation between LLMs and GenAI apps. An MCP server can provide pre-built prompt templates for certain tasks, and plain old data context for others. It allows your LLM to ingest reference data, or follow complicated workflows, without relying solely on APIs. Built to support rich interactions, MCP is especially useful for coding or complex decision making that may require multiple interactions with different data sources.

MCP brings an easily scalable approach to enhancing LLMs, giving them access to the fresh, trusted data they crave while allowing AI agents to tap into knowledge bases, DevOps tools, and enterprise systems. 

 

Model context protocol comes in handy when real-time, rich, and personalized context is needed for LLMs or AI agents. MCP is great at serving multi-source enterprise data – especially in regulated or high-trust environments but less suitable for analytics. It’s not a replacement for data lakes, ETL, or MDM, but rather an operational layer for serving context into AI apps.

Some enterprises underestimate the need for high-quality metadata for clarity on meaning, access policies, update times, and ownership. Poor context leads to poor responses.

Even with a single MCP server, performance bottlenecks can arise if underlying systems are slow or if the context isn’t focused enough. And security risks increase if permissions or redaction rules are not enforced every step of the way.

Companies can address these concerns by retrieving only necessary data, enforcing guardrails, conducting audits, and monitoring data freshness. Ongoing review and testing of prompts and agents can also help identify blind spots or risky flows early on in the process.

MCP is quickly evolving, with new tools and standards emerging for context delivery and prompt management. Keeping pace requires adopting best-in-class vendors, monitoring updates to the protocol, and selecting technology that evolves with the open ecosystem.

Enterprises gain the most from MCP by choosing a universal, future-ready server that can adapt as the protocol and marketplace develop. Best practices include:  

• Using a single multi-source MPC server, where your enterprise data is already well documented and governed 

• Getting IT, security, and business teams aligned on MCP from the get-go 

• Acquiring automated tools for lineage and compliance to scale safely 

GenAI Data Fusion, the RAG tool by K2view, acts as a single MCP server for any enterprise. Instead of building unique integrations for each LLM or AI project, every data product, whether sourced from the cloud or from legacy systems, is discoverable and served through the MCP protocol – bringing true business context and scale to your GenAI apps.

K2view is unique in its ability to work with both structured and unstructured data. MCP ensures that the K2view platform serves only the most current, relevant, and protected data to LLMs and agentic AI workflows.

GenAI Data Fusion delivers: 

• Privacy 

  Only authorized data is sent, with masking and redaction enforced. 

• Conversational latency 

  All context is delivered in real-time with no lag – critical for chat and agent interactions. 

• Complete auditability 

  Each context package can be traced, and every access is logged for compliance. 

These features are essential for regulated industries, and for any enterprise where fresh answers and trustworthiness are required.  

Survey data from our State of Data for GenAI report shows only 2% of businesses are currently ready for GenAI at scale, the biggest barriers being the inability to access fragmented data, poor lineage, and privacy gaps. With MCP, the K2view platform overcomes all of these challenges. 

 

Model Context Protocol (MCP) redefines how enterprise data is accessed and delivered for LLMs and AI agents. It bridges the gap between fragmented, multi-source environments and the need for real-time, governed, and high-trust context. MCP is supported by open standards, strong industry backing, and proven reference implementations.

By acting as a universal MCP server, K2view is the easy way to adopt model context protocol. It enables LLMs and AI agents to enhance responses with business context derived from every system in your organization.

Learn why the K2view RAG tool is the only MCP server you’ll ever need.