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

Adds caching behavior ('cached independently') and mentions the combination of multiple sources. Annotations already indicate readOnlyHint, so the description adds value without contradiction.

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?

Two sentences, front-loaded with main purpose, then supporting details. No wasted words.

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?

Covers all essential aspects: purpose, data types, caching, defaults. Output schema exists, so return details are adequately handled.

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?

Schema coverage is 100%, but description adds default values for 'include' parameter (profile,stock-summary,holdings,sectors) and lists available types, supplementing the schema.

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?

Description clearly states it retrieves aggregated ETF data in one call, listing the combined data sources. This distinguishes it from individual sibling tools like get_company_profile or get_news.

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?

Provides clear context: use for aggregated data, defaults, and optional types. Implicitly suggests alternatives (individual tools for single data types) but lacks explicit when-not-to-use.

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