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When you give an agent a set of tools, the model has to do something subtle on every turn: read all the tool descriptions and pick the right one for the current step.

This is a selection task. The more options you add, the harder that choice becomes. A focused toolset keeps selection sharp; an overloaded one makes the model hesitate, mis-route, or grab the wrong tool.

This lesson answers a deceptively simple question: how many tools should one agent hold?

The practical sweet spot is 4-5 tools per agent. At this size the model can reliably reason about which tool fits each step.

As the count climbs, selection reliability degrades. By around 18+ tools on a single agent, the model starts confusing similar options and choosing poorly. More tools does NOT mean more capability — past a point it means less reliable capability.

• 4-5 tools → optimal selection
• 18+ tools → degraded selection reliability

Here is a support agent with a tight, role-scoped toolset. Four tools, each with a clear job: verify the customer, look up their order, process a refund, and escalate to a human.

The model never has to wonder which of twenty near-identical tools to call. Each one maps to a distinct intent.

An important nuance: the model selects tools primarily from their descriptions, not their names. A good description states the tool's purpose, its return values, input formats with examples, edge cases, and applicability boundaries.

This is why tool count and tool quality interact. Even 5 tools will mis-route if their descriptions overlap or are ambiguous. The more tools you pile on, the more likely two of them sound alike, and the more selection errors you get.

Imagine cramming an entire department onto one agent: customer tools, order tools, billing tools, inventory tools, shipping tools, analytics tools... twenty-plus entries in one tools array.

Now several of them sound similar — lookup_order, find_orders, get_order_history, search_purchases. The model has to disambiguate on every turn, and its accuracy drops. Too many tools per agent is a recognized anti-pattern, right alongside ambiguous descriptions.

The fix is not to make descriptions ever longer — it is to scope tools to the role. Ask: what is THIS agent's job, and what is the minimum set of tools it needs to do it?

A refund agent needs identity, order, refund, and escalation tools. It does not need inventory forecasting or marketing analytics. Trimming irrelevant tools removes distractors and sharpens every remaining selection.

When a task genuinely needs many capabilities, you do not put them all on one agent. You use a hub-and-spoke multi-agent design: a coordinator decomposes the work and delegates to specialized subagents, each with its own narrow toolset.

Twenty tools spread across four subagents (5 each) selects far more reliably than twenty tools on one agent. Remember: subagents do NOT inherit the coordinator's history, so each subagent prompt must carry its own context explicitly.

Splitting tools across subagents brings a bonus: least privilege. Each AgentDefinition declares only the allowed_tools it actually needs.

A read-only research subagent never gets a process_refund or delete tool, so it cannot misfire one. Smaller, role-scoped toolsets are both more reliable (better selection) and more secure (narrower blast radius). One coordinator rule to remember: the coordinator's allowedTools must include "Task" so it can delegate.

Not everything an agent needs has to be a Tool. In MCP, server primitives split into three kinds:

• Tools — actions the model invokes
• Resources — read-only data/context like schemas or catalogs
• Prompts — reusable templates

If the model just needs to read a schema or a product catalog, expose it as a Resource, not a Tool. That keeps your tools array lean and reserved for genuine actions — another lever for staying near 4-5 actionable tools.

Claude Code's built-in toolset is a good model of disciplined scoping. Each tool has one crisp job:

• Glob — find files by pattern (e.g. **/*.test.tsx)
• Grep — search file contents
• Read / Write / Edit — load, create, precisely change files
• Bash — run shell commands

None of them overlap. The model composes them in an incremental flow — Grep entry points, Read files, Grep usages, Read consumers — rather than choosing among redundant options.

Before shipping an agent, run this check:

• Is the toolset near 4-5 tools, and well under 18?
• Does each tool map to a distinct intent with a non-overlapping description?
• Are read-only needs modeled as Resources, not Tools?
• If you need more capabilities, can you split into subagents with least-privilege toolsets?

If you are stretching one agent past a dozen tools, that is the signal to decompose — not to write longer descriptions.

Apply the rule to a real design decision.

Key takeaways:

• 4-5 tools per agent is optimal; reliability degrades as you climb, and 18+ tools noticeably hurts selection.
• The model selects from descriptions, so overlapping or ambiguous tools cause misrouting even at small counts.
• Scope tools to the role — trim distractors instead of writing longer descriptions.
• Need more capability? Split into subagents (hub-and-spoke) with least-privilege toolsets; pass context explicitly since subagents don't inherit history.
• Model read-only needs as MCP Resources, not Tools, to keep the tools array lean.