# Granularity and Naming — AI Agents and Tool Use

Source: https://www.geekswithgeeks.com/en/ai-agents-mcp/design-granularity

> Choose tools that match the user's tasks, not your internal API, and name them clearly.

## Not too many, not too tiny

Wrapping every REST endpoint as a tool gives the model dozens of low-level choices and forces many steps for one task. Instead design around **tasks**: `schedule_meeting(attendees, time)` rather than separate calls to list calendars, find free slots and create events. Use verb-noun names (`search_orders`, `cancel_order`) and keep related names consistent.

## A tool is an interface for a model

Design tools like an API for a careful but literal reader: clear names, narrow purpose, helpful errors.

![Three qualities: clear, safe, helpful.](assets/figures/ai-agents-mcp/section-3-map.svg) — Figure 3.1 — Clear, safe and helpful tools.

## Low-level vs task-level

The task-level tool hides orchestration inside your code, where it is testable and deterministic.

```text
Low-level (4 model steps):
  list_calendars -> get_free_slots -> pick_slot -> create_event

Task-level (1 model step):
  schedule_meeting(attendees=["asha","ravi"], duration_min=30)
  # your code finds a slot and creates the event
```

## Merge overlapping tools

If two tools have near-identical descriptions the model will confuse them. Merge them or make the difference explicit in each description.

**Quiz:** Why prefer a task-level tool over many tiny endpoint wrappers?

- [ ] It makes descriptions unnecessary
- [ ] Tiny tools are illegal
- [x] It reduces model steps and moves orchestration into testable code
- [ ] Models cannot call small tools

*Answer:* It reduces model steps and moves orchestration into testable code. Fewer, task-shaped tools mean fewer chances for the model to choose wrongly.