Building Agents with Claude: Tools, Reasoning, and the MCP Standard

Core Components

Reasoning and Long Context

Claude’s primary strengths for agent workflows are its ability to ingest massive amounts of text and its careful approach to problem-solving. Anthropic trains its models using a framework that prioritizes harmless and honest behavior. In an agent context, a model that pauses to ask for clarification is vastly superior to one that confidently executes the wrong action.

Tool Use and Function Calling

Through the API, you can provide Claude with a list of tools. The model does not run the code itself. Instead, it decides which tool is needed, formats the required inputs, and pauses. Your application executes the tool, feeds the result back to Claude, and the model continues reasoning. This creates a continuous loop of reasoning, acting, and observing.

The Universal Adapter

Historically, developers had to write custom API integrations for every single tool they wanted an AI to use. Anthropic introduced MCP to solve this problem. MCP provides a standardized interface for connecting models to data. If you write an MCP server for your database, any MCP-compatible agent can immediately understand how to query it.

The Agent SDK

Writing the loop that handles tool calling, error recovery, and state management gets complicated quickly. The Claude Agent SDK provides a pre-built harness for this control flow. It is the exact same underlying architecture that powers Claude Code.

Screen Control via Computer Use

Not all software has an API. Computer Use allows Claude to interact with user interfaces exactly like a human would. While currently slow and highly experimental, it represents a massive shift in how automated systems might interact with legacy software.

Advice for Beginners and Freshers

If you are just starting out with AI engineering, jumping straight into autonomous agents can be overwhelming. Keep these principles in mind:

• Start linear: Before building a loop where the AI decides what to do, build a simple script where the AI extracts data and calls one specific function.
• Learn prompt engineering: Agents live and die by their system prompts. You must learn how to write clear, unambiguous instructions before you introduce tool calling.
• Watch your costs: Claude’s large context window is powerful, but sending an entire codebase to the API on every single loop iteration will burn through your budget immediately. Learn to filter and search data before feeding it to the model.

A Basic Implementation

Building a tool-calling loop requires defining your tools, calling the API, and handling the response.

# A conceptual example of providing tools to Claude tools = [ { "name": "search_documentation", "description": "Search the internal engineering wiki", "input_schema": { ... } }, { "name": "create_jira_ticket", "description": "Open a new bug ticket", "input_schema": { ... } }, ] response = claude.messages.create( model="claude-3-5-sonnet-latest", system="You are a technical support agent. Search docs before opening tickets.", tools=tools, messages=[{"role": "user", "content": "The production database is locking up."}], ) # The model will return a tool_use block # Your code executes the search, returns the data to Claude, and loops 

Common Pitfalls to Avoid

Expecting an Out-of-the-Box Solution

Anthropic provides excellent infrastructure, not a turnkey business application. You are responsible for assembling the pieces, handling the error states, and building the user interface.

Ignoring System Guardrails

A well-trained model does not replace application security. You still need strict step budgets to prevent infinite loops, and you must implement human-in-the-loop approval gates for any action that modifies data or spends money.

Hardcoding API Integrations

If you are writing custom Python scripts to connect Claude to Slack, GitHub, or Postgres, you are creating technical debt. Invest the time to wrap those integrations as MCP servers so you can reuse them across different projects and models.

Testing Computer Use in Production

An agent that controls a real machine can accidentally delete files, send unintended emails, or break system configurations. Always run computer use experiments inside isolated Docker containers or dedicated virtual machines.

Advanced Concepts for Agent Builders

Once you have the basic tool-calling loop working, you will quickly run into real-world challenges. Here are a few essential concepts to master as you scale your agent.

Designing an Effective System Prompt

An agent is only as smart as its instructions. Beginners often write vague system prompts like “Be a helpful coding assistant.” This leads to unpredictable behavior when the model has to choose between multiple tools. Instead, define strict rules, boundaries, and a clear persona. Tell the agent exactly what to do when a tool fails or when it lacks enough information.

Managing Agent Memory

The Claude API is stateless. It does not remember the conversation from one request to the next. In an agent loop, you are responsible for maintaining the history of messages, including every tool call and tool result. As the conversation grows, so does the payload you send to the API. Learning when to truncate old messages or summarize past context is a critical skill for keeping your agent fast and reliable.

Leveraging Prompt Caching

Agent loops are repetitive by nature. You are sending the exact same system prompt and the same list of tools to the API over and over again. Anthropic offers a feature called prompt caching, which allows you to cache large chunks of context, like your tool definitions or massive reference documents. Using this effectively can reduce your API costs by up to 90% and make your agent respond significantly faster.

Debugging the Loop

When a standard script fails, you get a stack trace. When an agent fails, it might just confidently give you the wrong answer or get stuck in an infinite loop calling the same tool. To build effective agents, you need to log everything. Do not just print the final response to the console. Log the exact JSON payload the model sent to your tool, the exact output your tool returned, and the model’s internal reasoning before it made the call.

A Note on Evaluation

How do you know if your agent is actually getting better when you change its code?

If you are new to this field, avoid relying on ‘vibes’ to test your agent. Create a spreadsheet of ten common user requests and the exact actions the agent should take for each. Every time you update your system prompt or add a new tool, run the agent against those ten requests. Automated, repeatable testing is the only way to build production-ready AI systems.

Where to Go From Here

If you are following this series, building a custom Claude agent is the natural next step after exploring the Model Context Protocol. The underlying architecture across the industry remains the same: a reasoning loop, well-defined tools, clean context, and strict safety guardrails. Anthropic simply provides some of the best raw materials available to build it.

disclaimers: Image(s) generated using AI.

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