ReliaStats

explain_pi_vs_ci_for_validation

Read-onlyIdempotent

Return the specific explainer for the ReliaStats Interrupt Validation scatter chart's red y=x / blue 95% Prediction Interval / teal 99% Confidence Interval reference lines. Use when a user asks 'what do the bands mean' / 'why is my point outside the blue line' / 'how do I read the validation scatter'. The bands are FIXED plotting conventions — they are NOT recomputed from the loaded data; this is anti-fab by design. Text sourced from docs/reliability-theory.md (the 'Confidence intervals vs prediction intervals' sub-section of Advanced Reliability Patterns).

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description adds critical behavioral context: the bands are fixed plotting conventions and are NOT recomputed from loaded data. This anti-fabrication note and the mention of the source document provide transparency that the agent needs to avoid misleading users.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences with a clear structure: main purpose, usage triggers, and important behavioral caveat. It is appropriately sized—no fluff, but it includes essential details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only explainer tool with no output schema, the description is complete: it covers purpose, specific usage triggers, the critical 'fixed conventions' behavior, and the doc source. This provides everything an agent needs to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the baseline of 4 applies. The description correctly focuses on purpose and usage rather than parameter details, and no additional parameter information is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns the specific explainer for the validation scatter chart's reference lines, naming the exact colors and chart type. This distinguishes it from sibling explain tools (e.g., explain_reliability_basics) by focusing on the validation scatter context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly lists user queries that should trigger this tool ('what do the bands mean', 'why is my point outside the blue line', 'how do I read the validation scatter'), giving clear when-to-use guidance. It does not mention alternatives, but the specificity makes the intended usage unambiguous.

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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TDQS

A4.2/5.0

Disambiguation3/5

Several tools overlap in purpose, particularly the explain_* family: explain_distributions_for_reliability and recommend_distribution both address distribution selection, and explain_distributions also covers Weibull β interpretation, overlapping with interpret_weibull_shape. However, descriptions are detailed enough that careful reading usually disambiguates, so the confusion is moderate rather than severe.

Naming Consistency4/5

Most tools follow a clear verb_noun pattern (compute_, describe_, explain_, interpret_, list_, recommend_), making the naming predictable. Two exceptions, 'system_reliability' and 'weibull_summary', are noun phrases without a verb, which is a minor deviation from the pattern but not disruptive.

Tool Count5/5

With 11 tools, the server is well within the ideal 3–15 range and each tool serves a distinct purpose, from educational explainers to closed-form calculation utilities. The count feels well-scoped for a reliability statistics knowledge and reference server.

Completeness4/5

The tool surface covers fundamentals, distribution guidance, Weibull calculations, availability, system reliability, and paired-model documentation, giving broad coverage of the domain. It intentionally defers fitting to an external sandbox, so the main minor gap is the lack of direct data-fitting or confidence-interval computation tools, but these can be worked around.

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