Apr 04, 2025 by admin

The Model Context Protocol (MCP) is an open standard (open-sourced by Anthropic) that defines a unified way to connect AI assistants (LLMs) with external data sources and tools. Think of MCP as a USB-C port for AI applications – a universal interface that allows any AI assistant to plug into any compatible data source or service. By standardizing how context is provided to AI models, MCP breaks down data silos and enables seamless, context-rich interactions across diverse systems. 

In practical terms, MCP enhances an AI assistant’s capabilities by giving it controlled access to up-to-date information and services beyond its built-in knowledge. Instead of operating with a fixed prompt or static training data, an MCP-enabled assistant can fetch real-time data, use private knowledge bases, or perform actions on external tools. This helps overcome limitations like the model’s knowledge cutoff and fixed context window. It is observed that simply “stuffing” all relevant text into an LLM’s prompt can hit context length limits, slow responses, and become costly. MCP’s on-demand retrieval of pertinent information keeps the AI’s context focused and fresh, allowing it to incorporate current data and update or modify external information when permitted. 

Another way MCP improves AI integration is by unifying the development pattern. Before MCP, connecting an AI to external data often meant using bespoke integrations or framework-specific plugins. This fragmented approach forced developers to re-implement the same tool multiple times for different AI systems. MCP eliminates this redundancy by providing one standardized protocol. An MCP-compliant server (tool integration) can work with any MCP-compliant client (AI application). In short, MCP lets you “write once, use anywhere” when adding new data sources or capabilities to AI assistants. It brings consistent discovery and usage of tools and improved security. All these benefits make MCP a powerful foundation for building more capable and extensible AI assistant applications.

MCP Architecture and Core Components

At its core, MCP follows a client–server architecture that separates the AI assistant (client/host side) from the external integrations (server side). The design involves three primary roles:

• MCP Host: The AI assistant application or environment that needs external data or actions. This could be a chat interface, an IDE with an AI coding assistant, a CRM with an AI helper, etc. The host is where the user interacts and the LLM “lives”.
• MCP Client: This component (often a library within the host app) manages the connection to one or more MCP servers. It acts as a bridge, routing requests from the AI to the appropriate server and returning results. The client handles messaging, intent analysis, and ensuring the communication follows the MCP protocol format.
• MCP Server: A lightweight program or service that exposes specific capabilities (tools, data access, or context) through the MCP standard. Each server is essentially a context provider; it can fetch information from certain data sources or perform particular actions and return results in a structured way.

To visualize this, imagine the AI assistant as a laptop and each MCP server as a device or accessory that can be plugged in. The MCP client is like the universal hub/port that allows the computer to connect to many devices using the same interface. For example, the host AI (e.g., Claude or ChatGPT) connects via an MCP client “hub” to multiple MCP servers (adapters) that provide access to different services (Slack, Gmail, Calendar API, or local files). No matter who built the tool or data source, if it speaks MCP, the assistant can use it seamlessly. Each MCP server (bottom) is a context provider connecting the AI to a specific external service or data (icons for Slack, Gmail, Calendar, local files). The MCP client (middle, represented by the hub) enables the host AI application (top) to communicate with these servers through the standardized MCP interface. This modular design lets AI assistants plug into new data sources as easily as adding a new device, without custom integration for each tool.

Context Providers (MCP Servers)

Context providers are the external data sources or tools that an AI assistant can access via MCP. In MCP terms, these correspond to the MCP servers; each server provides a certain “capability” or data domain. For example, one MCP server might give access to a collection of documents or a knowledge base, another might interface with an email API, another with a database, and so on. The key is that each server follows the MCP standard for requests and responses, making them interchangeable from the perspective of the AI client. 

MCP servers can interface with local data sources (like files on your computer, local databases, etc.) or remote services (like web APIs, cloud apps). Indeed, a growing list of pre-built MCP servers already exists; for example, reference implementations are available for web searching, file operations, database queries, etc. You effectively make those data sources available to your AI by running or deploying the appropriate servers. The AI doesn’t need to know the low-level API details; it just sends a standardized request (e.g., “search for X” or “read file Y”), and the MCP server handles the rest. This design keeps the LLM isolated from direct external access. The server mediates what the AI can see or do, allowing for security and access control. In summary, context providers enable secure, plug-and-play integration of diverse data sources into the AI’s world.

Document Indexing and Retrieval

MCP servers often employ document indexing behind the scenes to efficiently use external data (especially large text corpora). Instead of storing a whole document or database record as one big blob, the data is pre-processed into an index that the server can query quickly. For textual data, this typically means splitting documents into chunks (e.g.,  paragraphs or passages) and converting them into a format suitable for fast similarity search, often embedding the text into vectors and storing them in a vector index or database. This is analogous to how a search engine indexes websites to retrieve relevant pages for a query instantly.

Why index documents? So that when the AI asks something, the server can find the relevant information without sending the entire data store. This is the essence of Retrieval-Augmented Generation (RAG): the user’s query is used to fetch relevant documents or snippets (via semantic search or keyword search), and those results are provided to the model as additional context. Using an index, the system can locate the needed knowledge quickly and accurately, even from large volumes of text. For example, if an AI can access a PDF library or a corporate wiki via MCP, the server might index all PDFs or wiki pages by content. When asked a question, it can then return just the top relevant sections to the AI rather than the AI scanning everything blindly. This speeds up the response and helps fit the info into the LLM’s context window limits.

It’s worth noting that MCP itself doesn’t mandate a specific indexing technique; depending on the server’s implementation, it could be a vector similarity search, a keyword-inverted index, a database query, etc. The protocol just standardizes how the AI can request and receive information. Indexing is one of the best practices for context-providing servers to ensure the AI gets the right data when needed.

Query Resolution Process

When a user asks a question or gives a prompt to an MCP-enabled AI assistant, the system goes through a query resolution workflow to figure out how to get the necessary context. In a typical MCP interaction, the process works like this: the user’s query goes to the MCP client (in the host app), which then analyzes the query’s intent and requirements. Based on this analysis, the client decides which context provider (MCP server) can best handle the request. For instance, if the query is “What are the steps to reset my email password?” the client might route this to a documentation or knowledge base server. The query “Schedule a meeting next Monday” might route to a calendar API server. The client essentially performs a tool selection or routing step.

Once the appropriate server(s) are identified, the client sends the request to the MCP server in a standardized format (e.g., a JSON RPC call defined by the MCP spec). The server then processes the request – this could involve running a search in an index (for a knowledge query), calling an external API, or performing some computation. For a data retrieval scenario, the server would execute a search or lookup on its indexed data. For example, it might take the query, run a semantic similarity search across document embeddings, and find the top matching chunks. The retrieved results (or action outputs) are then returned from the server to the client, which returns them to the AI model.

In many cases, the client might wrap the results into the prompt given to the LLM. This entire resolution cycle happens quickly and transparently. The user experiences the AI assistant responding with an answer or action outcome. Still, behind the scenes, the assistant may have consulted one or several external sources to get there. According to one description, the MCP client “selects the appropriate tools via the MCP server, and invokes external APIs to retrieve and process the required information before notifying the user of the results”. The architecture ensures that the communication is structured and secure at each step; the AI can only use the tools it’s allowed to and only in the ways the protocol permits.

A practical consideration in query resolution is that you typically only connect relevant providers for the task. An AI could have dozens of MCP servers available, but giving the model access to all of them simultaneously might be counterproductive. The best practice is to enable a subset of tools based on context or user scope to avoid confusing the model with too many choices. For instance, an AI agent in a coding IDE might load servers for Git and documentation but not the CRM or Calendar servers. This way, query resolution involves picking among a manageable set of options and reduces the chance of the model calling the wrong tool.

Context Delivery to the Assistant

After a provider fetches the relevant context, it needs to be delivered back to the AI model in a useful form. In an MCP setup, the server’s response is typically structured (e.g., containing the data or an answer). The MCP client then integrates that into the AI’s prompt or state. In a retrieval scenario, this often means attaching the retrieved text as additional context for the LLM to consider when generating its answer. For example, the client might prepend the model’s prompt with something like “Reference Document: [excerpt]…” before the actual question or use a special format the model is trained to understand (such as a system message with the context). The AI’s response is “enriched” with external knowledge; it can quote specifics from the provided text or base its reasoning on it. If multiple context pieces are returned, the client could concatenate them or present them in a list. The LLM will then see all those pieces and the user query and attempt to synthesize an answer. This dynamic injection of context means the AI can output information it didn’t originally know, effectively extending its knowledge at runtime. For the user, it feels like the assistant “knows” about internal documents or the latest news, when in reality, it is reading from the supplied context.

It’s important to highlight that context delivery in MCP is not limited to static text. While the focus here is on retrieval, MCP can also deliver the results of actions. For instance, if the user asks the AI to perform a calculation or send an email (and the MCP server for email executes that), the response delivered might be a confirmation or data about that action. In the case of retrieval (read-only context), the delivered content is analogous to what RAG provides: relevant documents for the model to read. However, MCP can go further; it supports active outputs. One source explains that RAG is read-only, whereas MCP enables the AI to “do things” and deliver the outcome. For example, an MCP server could return, say, “Email sent to John at 5 pm” as a result. In all cases, the final step is for the AI assistant to present the information or outcome to the end user in natural language. The user doesn’t see the raw context chunks or API calls; they just get the answer or confirmation, with the heavy lifting done via MCP behind the scenes.

In conclusion, the Model Context Protocol (MCP) advances the integration of AI assistants with diverse external data sources. MCP enables AI systems to dynamically leverage up-to-date, relevant information and seamlessly perform context-aware interactions by standardizing context retrieval, indexing, and delivery. This approach enriches the functionality and accuracy of AI assistants and simplifies development by establishing a universal framework, eliminating redundancy, and enhancing security.

Sources

• https://www.anthropic.com/news/model-context-protocol 
• https://docs.anthropic.com/en/docs/agents-and-tools/mcp 
• https://arxiv.org/pdf/2503.23278v1

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