compute_distribution

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

With no annotations, the description fully handles transparency. It states the tool is exact and read-only, and that no simulation is run. It does not detail error handling or performance, but covers core behavioral 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 a single paragraph that is relatively concise and front-loaded with the main purpose. It could be improved slightly by using bullet points for the metric list, but it is not verbose.

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 6 parameters with 100% schema coverage and an output schema mentioned, the description covers the two major usage modes and the key metrics. It lacks examples of parameter alignment but is sufficient for 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 coverage is 100%, baseline 3. The description adds significant meaning by explaining the 'metric' options (pdf, cdf, etc.) and the conditional use of 'at', 'family', 'parameters', and 'object_cell'. This goes beyond the schema's parameter names and types.

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 it is an analytic distribution calculator for computing distribution properties without simulation. It lists specific metrics and the two input modes (family/parameters or object_cell), distinguishing it from simulation-based sibling tools.

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?

Explicitly says to use for analytic computation without simulation, and that it is read-only and exact. It mentions alternatives by pointing at object_cell for fitted distributions, but does not directly contrast with simulation or fitting tools in sibling list.

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