Step-by-step articles on AI Agents, part of our AI & Machine Learning collection.
Showing 20 articles
"Agent" is used for many things, but the core idea is simple: an AI agent is an LLM that decides which tools to call, sees the results, and keeps going until the task is done . Your code runs the tools; the model chooses what to do next. The loop Send the user...
Before MCP, every AI application needed custom glue code for every integration: one plugin for GitHub in app A, a different one in app B. The Model Context Protocol (MCP) is an open standard, introduced by Anthropic, that defines one way for AI applications to...
Tool use (also called function calling) lets a model request that your code run a function. The model never runs code itself; it returns a structured request and waits for your result. 1. Describe the tools tools = [ { "name": "search_products", "description":...
In this tutorial you'll build an MCP server that exposes a get_github_repo tool, and run it over stdio so desktop AI apps can launch it. 1. Set up the project mkdir github-mcp && cd github-mcp npm init -y npm install @modelcontextprotocol/sdk zod npm install -...
The model chooses tools based only on their names, descriptions and schemas. A vague description is the most common reason an agent picks the wrong tool or invents arguments. Write descriptions like documentation for a new teammate { "name": "create_refund", "...
The official Python SDK includes FastMCP , a high-level API where type hints and docstrings become the tool schema. A working server takes only a few lines. 1. Create the project uv init notes-mcp && cd notes-mcp uv add "mcp[cli]" 2. Write the server # server....
The manual agent loop is always the same: call the model, run the requested tools, send results back, repeat. The Anthropic SDKs include a tool runner (currently in beta) that does this for you, with type-checked tool inputs. TypeScript with Zod import Anthrop...
Most MCP tutorials stop at tools, but the protocol has three server primitives, each controlled by a different party: Tools - the model decides when to call them. Resources - the application (or user) decides what data to attach. Prompts - the user explicitly...
When a request needs several independent lookups ("compare the weather in Delhi, Mumbai and Bengaluru"), the model can return multiple tool_use blocks in a single response. Running them one after another wastes time. Execute in parallel const toolUses = respon...
Once you have an MCP server, connect it to a host app so the model can use it. Here's how to do that in two popular hosts. Claude Desktop Open Settings, then Developer, then Edit Config. This opens claude_desktop_config.json . Add your server under mcpServers...
Reading data is low-risk. Sending emails, moving money or deleting records is not. A human-in-the-loop step pauses the agent and asks for approval before risky tools run. Classify your tools Read-only (search, get_*): run automatically. Reversible writes (draf...
Stdio servers run on the user's machine. A remote MCP server runs on your infrastructure and is reached over HTTP, which is how SaaS products expose their tools to AI apps. The current HTTP transport is Streamable HTTP : one endpoint that accepts JSON-RPC over...
An agent can only do what its tools allow. Good guardrails assume the model will sometimes make mistakes or be manipulated, and limit the damage when it does. 1. Least privilege Give each agent only the tools its job needs. Scope credentials: a read-only datab...
Host apps like Claude Desktop include an MCP client, but you can embed one in your own app too. That lets your product use any MCP server as a tool provider. Connect and list tools import { Client } from '@modelcontextprotocol/sdk/client/index.js'; import { St...
Every turn of an agent adds to the conversation: tool calls, large tool results, the model's replies. Long tasks can grow past the context window, and even before that, a bloated context makes the model slower, more expensive and less focused. 1. Keep tool res...
Debugging through a chat app is slow: you can't see the raw messages, and the model decides when to call your tool. The MCP Inspector is an official browser-based tool that connects to your server directly. Run it # TypeScript / Node server npx @modelcontextpr...
Agents are exciting, but they cost more, are slower and are harder to test than fixed code. Many features are better built as workflows : LLM calls arranged in a flow that your code controls. Start with the simplest thing One LLM call - classification, summari...
MCP connects models to real systems, so it inherits real security risks. Some apply to server authors, some to anyone installing servers. Risks for users and host apps Malicious servers. A local stdio server runs with your user's permissions. Only install serv...
Agents are non-deterministic, so traditional "input X gives exactly output Y" tests only go so far. A solid testing strategy combines normal tests for your code with task-level evaluations of the agent's behaviour. 1. Unit-test the tools Tools are ordinary fun...
If an MCP server is reachable over HTTP, the Claude API can connect to it for you. You list the server in the request and the API handles tool discovery and tool calls, so there's no MCP client or agent loop in your code. The request The MCP connector is a bet...
