Close-up of blue network wires connected together illustrating protocol-level data connections — Photo by Scott Rodgerson on Unsplash

What Is the Model Context Protocol (MCP)?

Q: What is the Model Context Protocol (MCP)?

MCP is an open standard introduced by Anthropic that defines how AI agents connect to external tools and data sources. It uses a JSON-RPC architecture with a host, client, and server model, so any MCP-compatible agent can connect to any MCP-compatible server without custom integration code.

Q: What is the difference between MCP and function calling?

Function calling lets a model request a specific tool execution during inference. MCP is the broader protocol that handles tool discovery, capability negotiation, and standardized communication between an agent host and external servers. MCP can wrap function calling as one of its transport mechanisms.

Q: Do I need MCP to build AI agents?

No. Teams can build agents using direct API calls and custom tool wrappers without MCP. MCP becomes valuable when you need to connect agents to many data sources and want to avoid building and maintaining bespoke integration code for each one.

Quick Definition#

The Model Context Protocol (MCP) is an open standard introduced by Anthropic that defines how AI agents connect to external tools, data sources, and APIs. Before MCP, every new tool integration required a custom connector written specifically for that agent and that tool. MCP replaces that fragmented approach with a single, consistent interface. Any agent that speaks MCP can connect to any MCP-compatible server — whether that server exposes a local filesystem, a SQL database, a REST API, or a SaaS application — without needing custom integration code for each one.

For foundational context, read What Are AI Agents? and explore more concepts in the AI Agents Glossary.

Why MCP Matters for Agent Deployments#

The integration problem has historically been one of the biggest friction points in production agent deployments. An agent that needs to read files, query a database, call a CRM API, and post to a ticketing system requires four separate integrations, each with its own authentication scheme, error handling logic, and maintenance surface. When the agent framework changes or the external service updates its API, each integration may break independently.

MCP addresses this by separating the protocol from the implementation. The protocol defines how a host (the agent runtime) discovers what a server can do, how it requests actions, and how errors and responses are structured. The server implementor writes that logic once. The agent developer never needs to know the internal details of how the server works — only what tools it exposes and what parameters they accept.

This matters especially for teams scaling from a handful of integrations to dozens. With MCP, adding a new data source to an agent is a matter of pointing the agent at a new MCP server, not writing new integration code.

For a practical comparison of frameworks that support MCP, see Best AI Agent Platforms and Open-Source vs. Commercial AI Agent Frameworks.

How MCP Works: The Architecture#

MCP uses a three-tier architecture: host, client, and server.

The Host#

The host is the agent runtime or application that the end user interacts with. Claude Desktop, an IDE plugin, or a custom agent application can all act as MCP hosts. The host is responsible for managing connections to one or more MCP clients and coordinating the overall agent session.

The Client#

The client lives inside the host. It maintains a one-to-one connection with a single MCP server and handles the protocol-level communication: sending requests, receiving responses, and managing the connection lifecycle. A single host can contain multiple clients, each connected to a different server simultaneously.

The Server#

The server is a lightweight process that exposes one or more tools, resources, or prompts through the MCP interface. Servers can wrap local capabilities (reading a directory, running a shell command) or remote services (querying a database, calling a REST API). Servers are designed to be narrow and focused — a filesystem server handles filesystem operations, a GitHub server handles repository operations, and so on.

The Transport Layer#

MCP supports multiple transport mechanisms. Standard input/output (stdio) is used for local server processes. HTTP with Server-Sent Events (SSE) is used for remote servers. The protocol is transport-agnostic, meaning the same server logic can be exposed over different transports without changes to the core implementation.

The JSON-RPC Message Format#

All MCP communication uses JSON-RPC 2.0, a lightweight remote procedure call protocol that uses JSON for message encoding. This choice keeps the protocol human-readable, language-agnostic, and easy to implement in any runtime. Messages follow a standard structure with method names, parameters, and response identifiers.

Close-up of blue network wires connected together illustrating protocol-level data connections

Tool Discovery and Capability Negotiation#

One of MCP's most important features is automatic tool discovery. When an agent connects to an MCP server, it can query the server for a list of all available tools, including their names, descriptions, and input schemas. The agent does not need to know in advance what the server can do.

This enables dynamic capability composition. An agent can connect to five different MCP servers at startup and present the combined tool catalog to the underlying language model. The model can then call any tool from any server without the agent developer having pre-wired each tool individually.

Tool descriptions in MCP include machine-readable JSON Schema definitions for parameters and human-readable text descriptions that help the language model understand when and how to use each tool. This dual-purpose design bridges the gap between structured execution requirements and natural language reasoning.

Practical Examples of MCP Servers#

Filesystem Server#

A filesystem MCP server exposes tools for reading, writing, listing, and searching files within a specified directory. An agent connected to this server can read configuration files, write outputs, and navigate project structures without the agent developer implementing any file I/O logic.

Database Server#

A database MCP server wraps a SQL or NoSQL database and exposes tools for querying, inserting, and updating records. The server handles connection pooling, query validation, and result formatting. The agent sends natural-language-derived queries through the MCP interface and receives structured results.

API Gateway Server#

An API gateway MCP server acts as an adapter between the MCP protocol and an existing REST or GraphQL API. This pattern is particularly useful for enterprise teams that want to expose internal services to agents without modifying those services or granting agents direct API credentials.

Web Search and Browser Servers#

MCP servers for web browsing and search let agents retrieve current information from the internet. These servers handle HTTP requests, HTML parsing, and content extraction, presenting clean text results to the agent through the standard MCP tool interface.

MCP and the Agent Framework Ecosystem#

MCP has been adopted by major agent frameworks and tooling providers. LangChain, LangGraph, and CrewAI have MCP integrations that allow developers to add MCP servers to their agent tool catalogs alongside custom tools. This means teams already using these frameworks can start using MCP servers incrementally without rewriting existing agents.

For teams using these frameworks, see Build an AI Agent with LangChain and Build an AI Agent with CrewAI for implementation context.

Security Considerations#

Because MCP servers can expose powerful capabilities — file system access, database writes, external API calls — security is a primary concern in any MCP deployment.

Key security practices include:

Scoped server permissions: Each MCP server should expose only the capabilities the agent legitimately needs, not broad access to underlying systems.
Input validation: Servers should validate all inputs before executing operations, rejecting malformed or out-of-bounds requests.
Authentication: Remote MCP servers should require authentication tokens or API keys; agents should store these securely and not expose them in logs.
Audit logging: Log all MCP tool calls and responses for security review and debugging.
Rate limiting: Protect servers from being overwhelmed by excessive agent-initiated requests.

For broader agent safety practices, see AI Agent Guardrails and AI Agent Observability.

MCP vs. Direct API Integration#

| Dimension | MCP | Direct API Integration | |-----------|-----|------------------------| | Setup overhead | Low after server exists | High per integration | | Tool discovery | Automatic | Manual/hardcoded | | Cross-framework portability | High | Low | | Custom logic flexibility | Moderate | Full | | Maintenance surface | Centralized in server | Per-integration |

Direct API integration gives maximum flexibility and control but requires writing and maintaining bespoke code for every external service. MCP trades some flexibility for a dramatic reduction in integration overhead — the right tradeoff for most teams connecting agents to multiple data sources.

Frequently Asked Questions#