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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool calculates a premium but doesn't describe how the calculation works, whether it's deterministic or approximate, if there are rate limits, or what the output format is. For a calculation tool with zero annotation coverage, this lacks critical behavioral context.

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 a single, efficient sentence that front-loads the core purpose. It wastes no words but could be slightly more structured by including usage context. It's appropriately sized for a simple tool, though it lacks depth that might be needed given the absence of annotations.

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 complexity (a calculation with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the calculation method, output format, or error conditions. The agent is left guessing about behavioral aspects, making this inadequate for reliable tool invocation.

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 description coverage is 100%, so the schema already documents all parameters with descriptions and constraints. The description adds no additional meaning beyond what the schema provides, such as explaining interactions between parameters or calculation logic. Baseline 3 is appropriate when the schema does the heavy lifting.

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 verb 'calculates' and the resource 'basic insurance premium', making the purpose evident. It distinguishes from sibling tools like 'calculate_health_premium' by specifying it handles multiple policy types, though it doesn't explicitly contrast with 'compare_policies'. The description is specific but could be more precise about its scope relative to siblings.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'calculate_health_premium' or 'compare_policies', nor does it specify prerequisites, exclusions, or appropriate contexts. Usage is implied by the parameters but not explicitly stated, leaving the agent to infer when this tool is suitable.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool calculates premiums including pre-existing conditions, but doesn't describe what the calculation returns (e.g., a numeric value, structured data), error conditions, rate limits, or authentication needs. For a calculation tool with zero annotation coverage, this leaves significant gaps.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every word earning its place.

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 complexity of a premium calculation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns (e.g., a premium amount, breakdown details), how pre-existing conditions affect the calculation, or any behavioral aspects. This leaves the agent guessing about the tool's output and usage.

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 description coverage is 100%, so the schema already documents all four parameters thoroughly. The description mentions 'including pre-existing conditions', which aligns with one parameter but doesn't add meaningful semantic context beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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 verb 'calculates' and the resource 'health insurance premium', specifying it includes pre-existing conditions. However, it doesn't explicitly differentiate from sibling tools like 'calculate_basic_premium' or 'compare_policies', which likely have related but distinct purposes.

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 provides no guidance on when to use this tool versus alternatives like 'calculate_basic_premium' or 'compare_policies'. It doesn't mention prerequisites, exclusions, or contextual factors that would help an agent choose appropriately among siblings.

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

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

No annotations are provided, so the description carries full burden. It states the tool 'compares premium' but doesn't disclose behavioral traits like whether it's read-only, what the output format is (e.g., list, table), or any constraints (e.g., rate limits, authentication needs). This is a significant gap for a tool with no annotation coverage.

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 a single, efficient sentence with zero waste—front-loaded and appropriately sized for the tool's purpose. Every word earns its place.

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 no annotations, no output schema, and a tool that performs comparison (implying potential complexity), the description is incomplete. It lacks details on output format, behavioral constraints, and how it differs from siblings, making it inadequate for full agent understanding.

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 description coverage is 100%, so the schema already documents both parameters (age, coverage_amount). The description adds no additional meaning beyond implying these are used for comparison, matching the baseline when schema does the heavy lifting.

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 action ('compares premium') and scope ('for all policy types'), with specific inputs ('given age and coverage'). It distinguishes from sibling tools by covering multiple policy types rather than specific ones like 'basic' or 'health', though it doesn't explicitly name the siblings.

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 implies usage when needing premium comparisons across policy types, but doesn't explicitly state when to use this versus the sibling tools (calculate_basic_premium, calculate_health_premium) or any exclusions. Context is clear but lacks alternative guidance.

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