distribution_stats

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds some context, such as the 'NOT AVAILABLE IN SYMPY-MCP!' warning and the default behavior of stats_to_compute, but it does not disclose side effects, supported distribution types, or error behavior. This is a moderate disclosure level.

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 starts with a clear one-sentence purpose, followed by sections for Args, Returns, and Examples. The 'NOT AVAILABLE' banner is prominent but adds valuable environment-specific context. Overall, it is well-structured and front-loaded, with only minor redundancy from repeating parameter names already in the schema.

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?

For a tool with 3 parameters, a nested object, no annotations, and an output schema not shown, the description provides essential usage details via the example and stats list. However, it omits supported distribution_type values and the exact structure of the parameters object, leaving room for misinterpretation. The output schema exists but is not elaborated, so completeness is moderate.

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 schema has 0% description coverage, so the description must compensate. The Args section names all three parameters, with stats_to_compute listing specific options and default, but distribution_type ('Type of distribution') and parameters ('Distribution parameters') are vague, leaving format and allowed values unclear. The example partially clarifies the parameter format (e.g., {"mean": "mu"}) but not exhaustively.

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 opens with a clear, specific verb-resource statement: 'Compute statistics of a distribution.' It lists the available statistics (mean, variance, std, skewness, kurtosis, entropy) and gives a concrete example, distinguishing it from sibling tools like distribution_probability (probabilities) and define_distribution (definition).

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 when to use the tool via its purpose and example, but it does not explicitly mention when not to use it or name alternatives. The sibling list includes related tools, but no direct guidance is provided, so the usage context is clear but not strongly differentiated.

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