Calculate key statistics—mean, median, standard deviation, variance, min/max, and 25/75/90 percentiles—from a list of numbers to summarize data distribution.
Compute mean, median, stdev, variance, min/max, and percentiles (25/75/90) for a list of numbers
| Name | Required | Description | Default |
|---|---|---|---|
| numbers | Yes | Array of numbers |
Changes observed during successful MCP inspections.
v2.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description bears the full burden of behavioral disclosure. It lists what is computed, but it does not disclose whether stdev/variance use sample or population formulas, how percentiles are interpolated, or whether the input is left unmodified. This leaves meaningful ambiguity for a statistics tool.
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, well-structured sentence that front-loads the main purpose and enumerates all expected outputs compactly. Every word adds value and there is no filler.
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 single-parameter pure computation tool, the description is mostly sufficient: it states the input and the statistical outputs. However, with no output schema and no annotations, it does not specify the return structure or the stdev/variance convention, so an agent may make incorrect assumptions about the result.
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 already describes 'numbers' as an array of numbers with minItems 1, giving 100% schema description coverage. The description only confirms 'list of numbers' and adds no new parameter-level meaning, so the baseline score of 3 is appropriate.
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 uses a specific verb ('Compute') and names the exact resource and outputs: mean, median, stdev, variance, min/max, and percentiles. This clearly distinguishes it from siblings like 'calculate' or 'count_tokens' without needing additional context.
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 phrase 'for a list of numbers' implies when the tool is appropriate, but there is no explicit when-to-use or when-not-to-use guidance and no comparison to alternatives. The usage context is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.