Calculate an exact one-sided Clopper-Pearson confidence limit from k observed failures in n trials. Use it to check what a k-of-n record actually supports before claiming success.
What a k-of-n record actually supports, as an exact one-sided Clopper-Pearson bound. 'It passed every time' is not a bound.
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | observed failures | |
| n | Yes | trials | |
| confidence | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It clearly states the exact one-sided Clopper-Pearson method and warns that k=0 is not a bound. However, it does not disclose whether the bound is lower or upper, how missing k is handled, or what edge-case behavior occurs, so transparency is incomplete.
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?
Two sentences with no redundant material, and the core statistical method is prominent. The phrasing is slightly indirect rather than a direct 'Computes...' command, but it is appropriately sized and memorable.
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 no annotations and no output schema, the description is too sparse operationally. It omits the direction of the bound, the default/behavior when k is omitted (only n is required), and any parameter validation or return-shape information. The statistical essence is present, but an agent cannot confidently invoke it correctly in all intended cases.
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 already labels k as observed failures, n as trials, and confidence with a default. The description adds useful 'one-sided' and 'k-of-n' context and clarifies that a perfect record is not a bound. It does not explain k's optionality/default or confidence range, so it remains around the baseline.
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 that the tool computes an exact one-sided Clopper-Pearson bound for a k-of-n record, which is a concrete and unambiguous purpose. The added warning that 'It passed every time' is not a bound reinforces the tool's role. It does not use an explicit imperative verb or name a sibling, so it misses the top score.
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?
No guidance is given about when to choose this tool over the sibling tools or about prerequisites. The only hint is a conceptual caveat about perfect records, not a selection criterion. An agent would have to infer usage from the statistical terminology.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.