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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond that: the data source ('computed from that bank's own FFIEC Call Report'), official citability, coverage ('2026 Q2'), and the 'Free, no key' and 'One institution per call' constraints. No contradiction with annotations.

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 slightly long but well-organized: the main action is front-loaded, followed by a list of returned metrics, scope, usage guidance, and constraints. Every sentence carries useful information; no filler. It could be trimmed slightly but remains appropriately sized for the tool's purpose.

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 single-parameter, read-only lookup with no output schema, the description is quite complete: it lists all returned metrics, specifies the data source, coverage date, institution count, and usage context. It does not describe the exact return format or behavior on invalid names, but the listed metrics and annotations provide enough for an agent to invoke it 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 input schema has 100% description coverage for the single 'name' parameter, already explaining it expects an institution name or distinctive part and a 3-character minimum. The tool description adds only minor reinforcement ('One institution per call') and does not substantially augment parameter semantics beyond the schema, 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 states a specific verb ('Look up'), a precise resource ('one US bank's balance-sheet stress metrics'), and enumerates the exact metrics it returns (AOCI burden, uninsured deposit ratio, non-performing loan ratio, total assets, state). It clearly differentiates from the sibling tool 'get_bank_stress_dataset_info' by focusing on individual institution lookup rather than dataset-level metadata.

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?

It gives an explicit trigger: 'Use this when an agent needs a grounded answer to "how stressed is this bank?" instead of guessing from memory or reading a filing by hand.' It also notes practical constraints ('Free, no key. One institution per call.') and scope ('Covers all 4,296 FDIC-insured institutions'). It does not explicitly mention the sibling tool or when not to use it, but the context is strong enough to route an agent.

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