Connecting LLMs to the Real World: Tool Use, Function Calling, and MCP

Introduction

Large Language Models (LLMs) are incredibly capable text synthesizers, but in isolation, they are completely cut off from the real world. They cannot read your local workspace, query your company's sales database, or send Slack notifications to coordinate a release.

To solve this, developers use **Tool Calling** (or function calling). In this paradigm, when an LLM determines it needs external information, it emits a structured JSON payload representing a tool request, which the client application executes. Recently, Anthropic introduced the **Model Context Protocol (MCP)**, standardizing this relationship into an open protocol.

Tool Calling and Function Selection

Tool calling is a three-stage communication loop. First, the developer passes a list of tool descriptions (schemas containing names, descriptions, and parameter constraints) along with the user's prompt.

Second, the LLM decides whether to run a tool. If yes, it pauses text generation and outputs a JSON object containing the function arguments. Third, the client application intercepts this JSON, runs the code locally or via APIs, and returns the string output to the LLM to resume text generation.

Tool Calling & Execution Pipeline

Observe how JSON parameters are generated by the model and executed by the local client

1. User Input

2. LLM Emits Tool Schema

3. Client Executes Function

4. LLM Compiles Output

Console Payload Output

Click Trigger Tool Call to run pipeline simulation...

The Model Context Protocol (MCP)

While tool calling is powerful, integration complexity quickly becomes a bottleneck. In a world without standards, every AI-powered client (Cursor, VS Code, Claude Desktop) has to implement unique connectors to talk to various tool servers (GitHub APIs, local SQLite, Jira hooks).

MCP introduces a standard client-server protocol. Under MCP, tool creators build a single **MCP Server** that exposes tool definitions via JSON-RPC. Any **MCP Host** (client editor or shell) that implements the standard immediately gains access to all MCP servers.

Interactive Integration Complexity Visualizer

Toggle between the two diagrams below to visualize how standardizing tool calls reduces architectural integration paths. What was once a custom mesh ($N \times M$) of duplicate API connectors becomes a clean, single-point star ($N + M$) topology.

Unified Protocol Integration Topology

Toggle between custom direct client integrations and the standardized Model Context Protocol

N x M Integration Mesh

Without a protocol standard, every AI editor/client must build custom drivers for every tool server (GitHub APIs, SQLite hooks, Slack webhooks).

The Scale Problem

Adding 1 new tool server requires updating 3 separate clients. Code logic becomes highly duplicated and fragile.

MCP simplifies tool calling into standard resources, prompts, and tool descriptions.

Interactive Tool Execution Console

(Click the Trigger button in the console above to watch how a standard tool calling loop parses parameters, verifies rights, and returns outputs.)

The Future of MCP Integrations

By standardizing how LLMs discover resources, request prompts, and call tools, MCP is building a modular ecosystem for AI applications. Rather than building custom database and API parsers for every new project, developers can plug in pre-configured MCP servers, giving their agents immediate, safe, and audited access to local development utilities.

Original system design topics compiled from the Anthropic MCP and ByteByteGo developer series.