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

With annotations already declaring readOnlyHint=true and destructiveHint=false, the description adds valuable context: it specifies the data source (FDIC BankFind synced call report data), the metrics returned (assets, deposits, capital ratios, loan quality, peer positioning), and provides an illustrative example with numeric thresholds and interpretation clarity. This goes beyond the annotation hints without contradicting them.

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 four sentences, each earning its place: a use-case opening, a concise list of return metrics, a concrete example with embedded interpretation, and a data-source attribution. No unnecessary filler, and the most important action-oriented phrase is front-loaded.

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?

Given the tool's low complexity (single required parameter, no output schema), the description is exceptionally complete. It explains when to use it, what data it returns, provides an example with realistic values, and identifies the underlying data source. The absence of an output schema is compensated by the detailed example and metrics enumeration.

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 input schema has 100% description coverage for bank_name, with examples like 'JPMorgan, Wells Fargo, First National Bank.' The description does not add additional parameter semantics beyond what the schema already provides, so the baseline of 3 applies.

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 evaluates a bank for acquisition, partnership, correspondent banking, or competitive analysis, and enumerates the returned data: assets, deposits, capital ratios, loan quality, and peer benchmark positioning. This distinguishes it from siblings like get_bank_regulatory_benchmark, which focuses on regulatory benchmarks rather than comprehensive financial intelligence.

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 description explicitly says 'Use when evaluating a bank for acquisition, partnership, correspondent banking, or competitive analysis in a local market,' providing clear context for when to invoke. However, it does not mention when not to use it or name specific alternatives among the many sibling tools.

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