The Model Context Protocol (MCP) serves as the interface layer between Large Language Models and the disparate ecosystem of data sources. Historically, connecting an LLM to a specific database, file system, or internal API required writing a custom adapter for that specific model-application pair. As the number of models () and data sources () grows, the complexity of maintaining these point-to-point integrations increases exponentially ().
MCP resolves this scalability issue by strictly defining a contract. Any server implementing the MCP specification can communicate with any MCP-compliant client. This shifts the complexity from an exponential curve to a linear one (), where developers only need to build a server once for a data source to make it universally available to supported AI assistants.
The specification does not dictate how the AI model processes information or how the database stores it. Instead, it standardizes the transport and message format used to exchange that information. This architectural pattern mirrors the way HTTP standardized web communication; browsers (clients) do not need to know the internal logic of a web server to render a page, provided both adhere to the HTTP standard.
In an MCP architecture, the specification mandates that communication occurs over a defined transport layer using JSON-RPC 2.0 messages. The protocol is transport-agnostic in principle but currently prioritizes standard input/output (Stdio) for local processes and Server-Sent Events (SSE) for remote HTTP connections.
The following diagram illustrates the structural shift from direct integration to the protocol-based approach.
Comparison of direct many-to-many integration versus the centralized protocol approach.
The MCP specification categorizes all interactions into three primary primitives. These primitives determine how data is exposed and how the model interacts with the server. A compliant server may implement one, two, or all three of these primitives depending on the use case.
Resources represent passive data that clients can read. They function similarly to GET requests in a REST API or file reads in a file system. Resources provide context to the model, such as the contents of a log file, a database schema, or the current state of a variable.
Resources are identified by URIs (Uniform Resource Identifiers). The server defines a list of available resources, and the client requests the content of a specific resource via its URI. This primitive is strictly for reading data; it does not perform side effects or modify the state of the system.
Prompts are pre-defined templates that help guide the interaction between the user and the model. While Resources provide raw data, Prompts provide structured instructions. An MCP server can expose a library of prompts that the client application can surface to the user.
For example, a server connected to a code repository might expose a prompt named review-code that automatically pulls relevant file contents (Resources) and wraps them in instructions for the LLM to perform a code review. This standardizes best practices for interacting with the specific data source.
Tools are executable functions that allow the model to perform actions or retrieve dynamic information that requires computation. Unlike Resources, Tools can have side effects. They are the equivalent of POST requests or function calls.
When a server exposes a Tool, it must provide a JSON Schema defining the expected arguments. The LLM uses this schema to generate the correct input parameters. The client then executes the tool on the server and returns the result to the model. This is the primary mechanism for enabling agents to perform tasks like querying a database with specific parameters or creating a ticket in a project management system.
The protocol enforces a strict initialization lifecycle. Before any data is exchanged, the Client and Server must perform a handshake. During this phase, both parties exchange a capabilities object.
The capabilities object declares which features are supported. For instance, a server might declare support for resources and tools but not prompts. Additionally, it might declare support for logging or sampling. This negotiation ensures backward compatibility and allows clients to degrade gracefully if a server does not support a specific feature.
Mathematically, we can view the capabilities negotiation as an intersection of sets. Let be the set of features supported by the client and be the set of features supported by the server. The active feature set for the session is:
This ensures that neither side attempts to invoke a protocol method that the other cannot handle.
The MCP specification places the security boundary at the transport connection level. The protocol itself does not enforce user authentication (like OAuth) within the JSON-RPC messages. Instead, it assumes that the transport layer is secure or that the server is running in a trusted environment (such as a local process spawned by the user).
Access control is managed by the host application (the Client). The Client is responsible for asking the user for permission before sending sensitive data to a server or allowing a server to execute a tool that might modify system state. This design keeps the server implementation simple while centralizing security decisions in the user-facing application.
Flow of control showing how user actions and LLM context needs map to specific MCP primitives.
Reliability is a significant component of the specification. The protocol defines standard error codes and messaging structures based on JSON-RPC norms. When a tool execution fails or a resource is unavailable, the server must return a structured error response rather than failing silently or crashing the connection.
Furthermore, MCP includes a dedicated logging primitive. This allows the server to push log messages (debug, info, warning, error) to the client. In a development environment, these logs appear in the host's debugging console (like the MCP Inspector), providing visibility into the server's internal state without polluting the standard output stream used for protocol communication.
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