9.3.1 Tools Roadmap: Schema, Permission, Observation

Tools move an Agent from language to action. More tools do not automatically make the Agent stronger; unclear tools create wrong calls, unsafe actions, loops, and cost leaks.

See the Action Boundary First

Tool calling should always be controlled: choose tool, validate arguments, check permission, run, observe, and decide the next step.

Run a Tool Schema Check

Use a schema before executing any tool call.

tool_call = {
    "name": "search_course_docs",
    "args": {"query": "RAG evaluation", "top_k": 3},
}

schema = {
    "name": "search_course_docs",
    "required": ["query", "top_k"],
    "max_top_k": 5,
}

name_ok = tool_call["name"] == schema["name"]
args_ok = all(field in tool_call["args"] for field in schema["required"])
limit_ok = tool_call["args"]["top_k"] <= schema["max_top_k"]

print("can_execute:", name_ok and args_ok and limit_ok)
print("observation_needed:", True)

Expected output:

can_execute: True
observation_needed: True

After the tool runs, the Agent must observe and summarize the result. Never let the model pretend a failed tool succeeded.

Learn in This Order

Step	Read	Practice Output
1	Function Calling	Convert model intent into structured action
2	Tool descriptions	Write purpose, inputs, limits, examples, and failure modes
3	Tool strategy	Choose tool order, fallback, timeout, and stop rule
4	Tool safety	Add permission, sandbox, audit, and human confirmation
5	Multi-tool practice	Record trace for successful and failed calls

Pass Check

You pass this chapter when you can read a tool trace and tell whether the failure happened in planning, parameterization, execution, observation, or permission control.

The exit mini project is a learning assistant with 3 tool schemas, 5 test calls, 1 failed-call record, and a printable trace.