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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds useful behavior beyond that: it discloses that results include exam deficiency rates, covers AI/ML and vendor-supplied models, cites specific sources, and even notes the cost. This gives the agent a solid sense of what the call will return.

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 longer than average but front-loaded with usage guidance, then covers return content, key domain facts, an illustrative example, sources, and cost. Each sentence adds value; the structure is logical, though it could be tightened slightly by moving the example after the source line or trimming duplicate regulatory references.

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 adequately explains what is returned: SR 26-2/OCC requirements by lifecycle stage, exam deficiency rates, and a concrete example finding. It also gives sourcing and pricing. It does not explain how institution_type changes the response, but for an optional single enum parameter this is not a significant omission.

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 has one optional enum parameter, institution_type, but no descriptions, and schema description coverage is 0%. The description does not mention this parameter or how it affects results. However, the enum values (community_bank, regional_bank, large_bank, etc.) are self-explanatory, and the default is provided in the schema, so the gap is minor.

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 and resource: it 'Returns Federal Reserve SR 26-2 and OCC requirements across development, independent validation, ongoing monitoring, and governance.' It also names the use case up front, which distinguishes it from generic regulatory benchmark siblings and makes the tool's niche clear.

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 when to use the tool: 'Use when preparing for a model risk management examination, building an SR 26-2 compliant model governance program, or assessing a financial institution's MRM framework against regulatory expectations.' It gives clear context but does not mention specific alternatives or exclusions, so it stops short of a 5.

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