pell_lucas_number

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only states what the tool calculates, not how it behaves: no information on input validation (e.g., n must be non-negative integer), computational characteristics (recursive vs. formula-based), error handling, or output format. For a mathematical computation tool, this is insufficient 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 extremely concise and front-loaded: a single sentence that directly states the tool's purpose with domain/category tags. Every word earns its place with zero redundancy or unnecessary elaboration, making it efficient for agent comprehension.

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 mathematical nature, no annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't explain what a Pell-Lucas number is mathematically, how it's computed, what the output looks like, or any edge cases. For a tool with one parameter but no structured documentation, this leaves significant gaps in 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?

The input schema has 0% description coverage, leaving parameter 'n' undocumented. The description adds minimal semantics by implying 'n' represents the index in the sequence ('nth Pell-Lucas number'), which is better than nothing. However, it doesn't specify constraints (e.g., n ≥ 0) or provide examples, so it only partially compensates for the schema gap.

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's purpose: 'Calculate the nth Pell-Lucas number (companion to Pell numbers).' It specifies the action (calculate), the mathematical object (Pell-Lucas number), and provides domain context. However, it doesn't explicitly differentiate from sibling tools like 'pell_number' or 'lucas_number', which would require a 5.

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 mentions 'companion to Pell numbers' which hints at relationship but doesn't specify use cases, prerequisites, or comparisons with similar tools like 'pell_number' or 'lucas_sequence'. This leaves the agent without practical usage direction.

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