🤖 Build Your First AI Agent Lesson 3 of 10

Tools and Capabilities

Lesson Content

knowledge + action + autonomy = capable agent

Three Categories of Tools

Tool Categories
01KnowledgeSearch, query, read -- expand what it knows
→
02ActionEmail, write, call APIs -- change the world
→
03AutonomyCron, webhooks, monitors -- work independently
Three categories power every capable agent.

Every tool an agent can use falls into one of three categories. Understanding these categories helps you design agents that have the right capabilities for the job:

Knowledge Tools — Expand What the Agent Knows

Give the agent access to information beyond its training data. Without these, the agent can only answer from what it learned during training — which is frozen in time.

Web Search Database Query File Reader Knowledge Base API Data Fetch
Action Tools — Let the Agent Change the World

Let the agent take real actions — send emails, create records, trigger workflows. These are what separate an agent from a chatbot. Without action tools, the agent can only talk about doing things.

Email Sender API Caller File Writer Notification Database Insert
Autonomy Tools — Let the Agent Work Independently

Enable the agent to operate without human prompting — triggers, schedulers, monitors. These turn a reactive agent into a proactive one that works while you sleep.

Cron Scheduler Webhook Listener Health Monitor Event Trigger Queue Consumer
name + description + input_schema = tool definition

How Tools Are Defined (Real Code)

In the Claude API, each tool is defined as a JSON schema. The description field is what Claude reads to decide when to use the tool:

# A knowledge tool: web search
{
  "name": "web_search",
  "description": "Search the web for current information. Use when the user asks about recent events, live data, or anything not in your training data.",
  "input_schema": {
    "type": "object",
    "properties": {
      "query": {
        "type": "string",
        "description": "Search query"
      }
    },
    "required": ["query"]
  }
}

# An action tool: send email
{
  "name": "send_email",
  "description": "Send an email. Use when the user asks to communicate with someone via email.",
  "input_schema": {
    "type": "object",
    "properties": {
      "to": { "type": "string" },
      "subject": { "type": "string" },
      "body": { "type": "string" }
    },
    "required": ["to", "subject", "body"]
  }
}
search + database + email = multiplied power

Tools Compound in Value

Each tool you add does not just add one capability — it multiplies them. Tools work together in ways that unlock tasks no single tool could handle:

SEARCH + EMAIL Research a topic from the web, then email a summary. Neither tool alone can do both.
DATABASE + API Look up customer data in your database, then call Stripe to check payment status. Full account audit in seconds.
MONITOR + NOTIFY Watch server health metrics, send Slack alert when CPU spikes. Proactive monitoring without human attention.
FILE + CALC + DB Read a CSV file, calculate totals accurately, save results to database. End-to-end data pipeline.

Choosing the Right First Tool

The right first tool depends on your agent's job. Match the tool to the most critical capability gap:

Research agent → Web search first. It needs to find current information.
Support agent → Database query first. It needs to look up customer accounts.
Coding agent → File read/write first. It needs to see and modify code.
Data agent → Calculator first. LLMs are unreliable at math — a calculator guarantees precision.

Why a Calculator Tool Exists

This surprises people: why does a powerful AI need a calculator? Because LLMs do not actually compute — they predict the most likely next token. For simple math, they are usually right. For precise calculations, they hallucinate:

LLM alone
"What is 47,832 × 891?"
Answer: 42,618,312
Wrong. (Actual: 42,618,312... sometimes right, sometimes not. You cannot trust it.)
LLM + Calculator tool
Agent calls calculator(47832 * 891)
Returns: 42,618,312
Always correct. Deterministic, guaranteed.

The agent's intelligence is in knowing when to delegate to a tool. The best agents are not the ones that try to do everything themselves — they are the ones that route each sub-task to the right tool.

Equip Your Agent with Tools

Here is how to define tools in the Claude API. Each tool needs a name, description (tells Claude WHEN to use it), and an input schema (tells Claude WHAT to send):

Python — defining tools for a Claude agent
# Tools are JSON schemas that tell Claude what it can do
tools = [
    {
        "name": "lookup_customer",
        "description": "Look up a customer by email. Use when the user mentions their account, billing, or subscription.",
        "input_schema": {
            "type": "object",
            "properties": {
                "email": {"type": "string", "description": "Customer email address"}
            },
            "required": ["email"]
        }
    },
    {
        "name": "web_search",
        "description": "Search the web. Use when the user asks about current events, prices, or information not in the knowledge base.",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"}
            },
            "required": ["query"]
        }
    },
]

# Pass tools to Claude — now it's an AGENT, not a chatbot
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    tools=tools,           # ← this is what makes it an agent
    messages=[{"role": "user", "content": "What plan is [email protected] on?"}]
)
# Claude will respond with a tool_use block:
# {"type": "tool_use", "name": "lookup_customer", "input": {"email": "[email protected]"}}
Zero tools = chatbot. One tool = capable. Three tools = agent. Each tool you add multiplies what the agent can do. A web search + database + email sender turns a text generator into a full support agent that can research, look up accounts, and follow up — all autonomously.

Tools and Agent Capabilities

knowledge reads | action writes | autonomy loops

1What capability does adding an API Caller tool unlock?

2Why does a Calculator tool exist when LLMs can attempt math?

3What is the difference between a knowledge tool and an action tool?

4An agent needs to: (1) look up a customer, (2) check their payment status, (3) send a refund notification. Which tool categories are needed?

5What makes tools compound in value?

Agent Tool Categories

Knowledge tools
Tools that expand what the agent knows — web search, database queries, file readers, knowledge base lookups. They bring information IN from external sources.
Action tools
Tools that let the agent change the world — email senders, API callers, file writers, notification systems. They push changes OUT to external systems.
Autonomy tools
Tools that enable independent operation — cron schedulers, webhook listeners, health monitors, event triggers. They let the agent work without human prompting.
Why tools compound in value
Each additional tool multiplies capabilities. Search + email = research and communicate. Database + API = full account audits. The combination enables tasks no single tool could handle.
Why LLMs need calculator tools
LLMs predict tokens probabilistically — they do not actually compute. For precise calculations, they can hallucinate. A calculator tool delegates math to a deterministic system that is always correct.
Choosing the right first tool
Match the first tool to the agent's primary job. Research agent → web search. Support agent → database. Coding agent → file read/write. Data agent → calculator.
Tool definition in Claude API
A JSON schema with name, description, and input_schema. The description tells Claude WHEN to use the tool. Include 'Use when...' for accurate tool selection.