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

With annotations already declaring readOnlyHint=true and destructiveHint=false, the safety profile is covered. The description adds behavioral context by citing the source ('FDIC BankFind synced call report data'), stating the per-call cost ('$0.02 USDC per call'), and giving a concrete output example with benchmark comparisons. It does not mention error handling, pagination, or rate limits, but the added source and cost details go beyond the minimum.

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 compact and front-loaded: immediate use case, then data content, then an illustrative example, then source and cost. Every sentence earns its place—the example clarifies output format, and the cost is essential for agents making cost-aware decisions. No filler or redundancy.

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?

For a tool with one parameter, no output schema, and annotations covering safety, the description provides a strong context: what data is returned, from what source, at what cost, and a realistic example with peer comparison. It does not specify the exact response structure or how to handle missing banks, but the example and field list make the expected output sufficiently clear for an agent.

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 for the single parameter bank_name is 100%, including examples ('e.g. JPMorgan, Wells Fargo, First National Bank'). The description does not add parameter-specific guidance, such as whether to use legal names, common trade names, or FDIC certificate numbers. The example in the description ('Midwest Community Bank') reinforces the idea of using a recognizable bank name, but it adds little beyond 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?

The description opens with a clear use context ('evaluating a bank for acquisition, partnership, correspondent banking, or competitive analysis') and then specifies the exact data returned: 'FDIC-sourced assets, deposits, capital ratios, loan quality, and peer benchmark positioning.' This goes beyond a generic verb+resource by naming distinct outputs and a data source, which differentiates it from siblings like get_bank_regulatory_benchmark or get_cra_performance_ratings.

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 states when to use the tool: 'Use when evaluating a bank for acquisition, partnership, correspondent banking, or competitive analysis in a local market.' It provides clear contexts, but it does not mention when not to use it or name alternative sibling tools, so it falls short of the top score.

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