distinct_partitions

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 counts partitions with distinct parts, implying a read-only operation, but doesn't mention computational limits (e.g., performance for large n), error handling, or output format. For a tool with zero annotation coverage, this is a significant gap in 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: it states the core functionality in one clear sentence, followed by domain and category in parentheses. Every word earns its place, with no redundancy or unnecessary elaboration, making it efficient for quick understanding.

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 (mathematical partition counting), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain the return value (e.g., integer count), handle edge cases, or provide usage examples. For a tool with these contextual gaps, more detail is needed to be fully helpful.

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 one parameter 'n' with 0% description coverage, so the schema provides no semantic information. The description adds minimal context by implying 'n' is the integer to partition, but doesn't specify constraints (e.g., n must be non-negative) or examples. This partially compensates but leaves key details undocumented, aligning with the baseline for low schema coverage.

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: 'Count partitions of n into distinct parts (no repeated values).' It specifies the verb ('count'), resource ('partitions of n'), and constraint ('distinct parts, no repeated values'), making the operation unambiguous. However, it doesn't differentiate from sibling tools like 'partition_count' or 'restricted_partitions', which likely handle different partition types, so it falls short of a perfect score.

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 the domain ('arithmetic') and category ('partitions'), but doesn't specify prerequisites, typical use cases, or comparisons to sibling tools such as 'partition_count' or 'restricted_partitions'. This lack of context leaves the agent without clear usage direction.

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