Confidence interval for a mean (t-based; from data, or n/mean/sd) or a proportion (Wilson; successes/n). kind = mean | proportion; confidence default 0.95.
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | ||
| sd | No | ||
| data | No | ||
| kind | No | ||
| mean | No | ||
| successes | No | ||
| confidence | 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?
With no annotations, the description carries the full burden. It openly discloses the statistical methods (t-based and Wilson), the two input modes (data vs. summary statistics), and the default confidence level (0.95). It stops short of describing edge cases, error behavior, or assumptions like normality, but the core behavioral choices are transparent.
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 dense sentence that front-loads the main purpose and then packs in the mode and parameter information. It is efficient but somewhat cryptic due to the heavy use of slashes and semicolons; a slight restructuring would improve readability.
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 7 parameters, no required fields, and no output schema, the description covers the essential modes and parameter roles but omits examples, return format, validation rules, and how parameters interact (e.g., whether data can be combined with mean). An agent can invoke it but may be uncertain about required parameter combinations.
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 description coverage is 0%, so the description must explain the parameters. It does so by grouping them: data or n/mean/sd for the mean, successes/n for the proportion, kind to select the mode, and confidence as the level. It maps the primary parameters clearly, though it does not spell out constraints like n > 1 or successes ≤ n.
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 specifies a clear purpose: computing a confidence interval for either a mean (t-based) or a proportion (Wilson). It distinguishes itself from sibling tools like hypothesis_test and descriptive_stats by naming its exact statistical output and method.
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?
Usage is implied: use when you need a confidence interval for a mean or proportion. However, there is no explicit guidance on when not to use it, no mention of alternatives, and no comparison to siblings such as hypothesis_test or bayes_update.
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.