Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/idempotent/destructive annotations, the description discloses critical behavioral nuances: response content depends on requester identity (operator gets extra fields, public callers don't), local stdio may show total_calls>0 with unique_users=0, and error_rate uses a 0-1 decimal while share_pct is a percentage. These are non-obvious but essential for correct interpretation, making the tool much safer to use.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear [Purpose], [Usage], [Response], and [Rules] sections, front-loading the core purpose. Though verbose, every section adds necessary detail for a tool with conditional behavior; a slight over-length is justified by complexity, but it could be tightened.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the full burden of explaining response structure, which it does thoroughly: including common fields, conditional operator-specific fields, and important rules about units and edge cases. An agent has everything needed to call the tool and interpret results correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already fully explains the 'days' parameter (trailing window, 0=entire period, default 30). The description adds only usage examples, not new semantic meaning. With 100% schema coverage, the description's contribution here is minimal, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns tool usage aggregates ('도구 사용량 집계를 반환합니다') and specifies exactly what metrics are included: per-tool call counts, popularity, latency, error rate, cache hits, and daily trends. This is distinct from sibling tools focused on financial data, audits, or documents, leaving no ambiguity about its purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The [Usage] section provides three concrete examples mapping natural language requests to parameter values: default 30 days, days=90 for recent trends, and days=0 for all-time cumulative. While it doesn't explicitly contrast with alternatives, no sibling tool serves this purpose, and the examples give clear when-to-use context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.