partition_count

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 mentions the method ('dynamic programming'), which hints at computational behavior, but does not describe performance characteristics (e.g., time complexity for large n), error handling, or output format. This is a significant gap for a tool with no annotations, as the agent lacks insight into its operational traits.

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 concise and front-loaded, with a single sentence stating the core functionality followed by domain/category tags. Every part earns its place, but the domain/category tags are somewhat redundant given the sibling context. It could be slightly more efficient by integrating the tags into the main sentence.

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 computation with one parameter), no annotations, and no output schema, the description is minimally adequate. It covers the purpose and method but lacks details on behavior, output format, and usage context. For a tool in a server with many siblings, more guidance is needed to ensure the agent can use it correctly without trial and error.

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 description must compensate. It adds meaning by specifying that 'n' is the integer whose partitions are counted, which is crucial semantic context not in the schema. However, it does not detail constraints (e.g., n must be non-negative) or examples, leaving some ambiguity.

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 the number of integer partitions of n using dynamic programming.' It specifies the verb ('Count'), resource ('integer partitions of n'), and method ('dynamic programming'), distinguishing it from siblings like 'distinct_partitions' or 'partitions_into_k_parts'. However, it does not explicitly differentiate from all siblings, such as 'generate_partitions', which might generate partitions rather than count them.

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 does not specify scenarios, prerequisites, or exclusions. For example, it does not clarify if this is for counting all partitions or distinct ones, or when to choose it over other partition-related tools in the sibling list.

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