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s01 → s02 → s03 → s04 → ... → s16 → s17

"Add a tool, add just one handler" — The loop stays the same. Register the new tool in the dispatch map and you're done.

Harness Layer: Tool Dispatch — Expanding the model's reach.

The s01 Agent has only one tool: bash. To read a file, cat; to write, echo "..." > file.py; to edit, sed.

The model thinks "read this file" but has to spell out cat path/to/file. An extra layer of translation that wastes tokens and invites errors.

The s01 loop is fully preserved (LLM call, tool_use block check, message append — not a single word changed). The only change is in that one line of tool execution: run_bash() is replaced with TOOL_HANDLERS[block.name]() dispatch lookup.

Adding a tool to the Agent requires just two things:

1. Define the tool: Add one entry to the TOOLS array
2. Register the handler: Add one mapping in the TOOL_HANDLERS dict

s01 had only bash:

TOOLS = [{"name": "bash", ...}]

def run_bash(command): ...

s02 expands to 5 tools, each independently defined:

TOOLS = [
    {"name": "bash",       "description": "Run a shell command.", ...},
    {"name": "read_file",  "description": "Read file contents.",  ...},
    {"name": "write_file", "description": "Write content to file.", ...},
    {"name": "edit_file",  "description": "Replace text in file once.", ...},
    {"name": "glob",       "description": "Find files by pattern.", ...},
]

Each tool has its own implementation function:

def run_read(path, limit=None):
    lines = safe_path(path).read_text(encoding="utf-8").splitlines()
    if limit:
        lines = lines[:limit]
    return "\n".join(lines)

def run_write(path, content):
    safe_path(path).write_text(content, encoding="utf-8")
    return f"Wrote {len(content)} bytes to {path}"

def run_edit(path, old_text, new_text):
    text = safe_path(path).read_text(encoding="utf-8")
    if old_text not in text:
        return "Error: text not found"
    safe_path(path).write_text(text.replace(old_text, new_text, 1), encoding="utf-8")
    return f"Edited {path}"

def run_glob(pattern):
    import glob as g
    matches = sorted(set(g.glob(
        pattern, root_dir=WORKDIR, recursive=True)))
    shown = matches[:200]
    if len(matches) > 200:
        shown.append("... (more matches omitted; narrow the pattern)")
    return "\n".join(shown)

TOOL_HANDLERS = {
    "bash":       run_bash,
    "read_file":  run_read,
    "write_file": run_write,
    "edit_file":  run_edit,
    "glob":       run_glob,
}

# Only one line changed in the loop — from hardcoded run_bash to dispatch lookup:
for block in tool_calls:
    handler = TOOL_HANDLERS[block.name]    # lookup
    output = handler(**block.input)         # call
    results.append(...)

Adding a tool = one entry in TOOLS array + one line in TOOL_HANDLERS dict. The loop stays the same.

The model often returns multiple tool_use calls at once — "read a.py and b.py, then list all .py files".

Calls are executed one by one in their original response.content order.

cd learn-claude-code
python s02_tool_use/code.py

Try these prompts:

1. Read the file README.md and tell me what this project is about
2. Create a file called test.py that prints "hello", then read it back
3. Find all Python files in this directory
4. Read both README.md and requirements.txt, then create a summary file

What to watch for: When does the model call just one tool, and when does it call multiple at once? Are multiple tool calls executed in the correct order?

The Agent now has 5 specialized tools. File tools are protected by safe_path, but bash is unrestricted — rm -rf / still runs.

→ s03 Permission: Add a gate before tool execution — is this operation safe? Does it need user approval?