Evaluate a probability distribution (normal, t, chi2, binomial, poisson): pdf/pmf and cdf at a value, and/or the quantile at a probability, plus mean & variance. Params per dist: normal {mean,sd}, t {df}, chi2 {df}, binomial {n,p}, poisson {lambda}.
| Name | Required | Description | Default |
|---|---|---|---|
| p | No | probability to get the quantile for (0-1) | |
| at | No | value to evaluate pdf/pmf and cdf at | |
| dist | Yes | ||
| params | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It clearly states which computations are available and which parameters each distribution expects, but it does not describe the return format, behavior when both 'at' and 'p' are supplied, or handling of invalid parameter values.
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 compact and front-loaded with the core purpose, followed by a dense but necessary per-distribution parameter reference. No redundant wording is present.
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 pure computation tool, the description covers the supported operations and all distribution-specific parameter schemas well, even without an output schema. It leaves minor gaps such as exact return shape and parameter value constraints, but the essentials for invoking the tool correctly are present.
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 only describes 'p' and 'at'; 'dist' has just an enum and 'params' is an untyped object. The description compensates by listing each distribution and its expected parameter keys, adding real meaning beyond the schema. It stops short of full specificity by omitting parameter types and constraints.
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 names the exact verb 'Evaluate' and the resource 'probability distribution', then enumerates the supported distributions and the computed quantities: pdf/pmf, cdf, quantile, mean, and variance. This is concrete enough to distinguish the tool from statistical siblings like hypothesis_test or linear_regression.
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 intended use is implied: call this when you need distribution-level calculations such as pdf, cdf, or quantile values. However, there is no explicit guidance about when not to use it or how it compares to sibling tools like confidence_interval or descriptive_stats.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct statistical operation: descriptive summaries, distribution calculations, hypothesis tests, confidence intervals, Bayesian updating, and linear regression. The only mild adjacency is between confidence intervals and hypothesis tests, but their descriptions clearly separate estimation from significance testing.
All six tool names follow the same lowercase snake_case pattern and are straightforward noun phrases describing the statistical concept. There is no mixing of conventions or inconsistent verb styles across the set.
Six tools is well within the ideal range and each one covers a broad, meaningful area of statistics. The count feels appropriately scoped without redundancy or bloat.
The set covers core statistical workflows: description, distributions, estimation, tests, Bayesian updates, and regression. Some common additions like ANOVA or nonparametric tests are absent, but they are not necessary for most basic statistics tasks.