Model Context Protocol (MCP)

Model Context Protocol (MCP) is a standardized interface that lets an AI model (typically an LLM) discover, request, and use external context—such as files, databases, internal tools, and application state—through well-defined “servers” and “tools,” with consistent schemas for inputs, outputs, and permissions.

What is Model Context Protocol (MCP)?

Model Context Protocol (MCP) is a protocol designed to make tool and data access for LLM-powered applications more interoperable. Instead of each application building one-off integrations (custom function-calling formats, bespoke auth, and unique payload shapes), MCP defines a common way for a “client” (the app or agent runtime) to connect to one or more MCP servers that expose capabilities. These capabilities can include reading a document, searching a knowledge base, running a command, or fetching customer records—while returning structured, machine-readable results.

A useful way to think about MCP is as “USB-C for AI context”: it standardizes how context providers plug into agentic systems. This standardization reduces integration overhead, encourages reuse of tools across different LLM vendors or frameworks, and helps teams manage access control and auditing more consistently.

Where MCP is used and why it matters

MCP is most commonly used in agentic AI systems where models must act on up-to-date or private information. Examples include coding agents that need repository context, enterprise assistants that need CRM or ticketing data, and research agents that need controlled web or internal search. MCP matters because it improves portability (swap tools without rewriting prompts), reliability (typed inputs/outputs instead of free-form text), and governance (centralized permissions and logging per server/tool).

Examples

• Filesystem/context server: An MCP server exposes tools like read_file, list_directory, or search_in_files, allowing an LLM agent to pull precise snippets rather than relying on manual copy/paste.
• Knowledge base/RAG server: A server offers semantic_search and returns top-k passages with metadata (source, timestamp, access scope), enabling more grounded answers.
• Business systems server: Tools like get_customer, create_ticket, or update_order_status let an assistant perform real workflows while keeping actions structured and auditable.

FAQs

Is MCP the same as function calling?
Not exactly. Function calling is usually a model-provider feature for producing structured tool calls. MCP is a broader interoperability layer: it standardizes how tools are exposed (servers, tool catalogs, schemas) and how clients connect to them, regardless of which model is used.

Does MCP replace RAG?
No. MCP can enable RAG by standardizing access to retrieval tools, but RAG is an architectural pattern for grounding generation with retrieved documents.

What should teams watch for when adopting MCP?
Focus on permissioning (least privilege), audit logs, and tool result schemas. Well-designed schemas and strong access controls matter as much as the model quality in agentic systems.