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ReliaStats

by com.reliastats

recommend_distribution

Read-onlyIdempotent

Given a free-text symptom description (e.g. 'manufacturing burn-in', 'bearing wearout under variable load', 'cosmic-ray bit flips'), return an ordered shortlist of distribution candidates with a one-line rationale per recommendation. Keyword-matched against a curated dictionary; ALWAYS treat output as a starting point for fitting work, not a fit. The actual fitting happens in the ReliaStats sandbox (protected/app.html). ANTI-FABRICATION: rationales are written ChiAha content; the algorithm is a deterministic substring match. Quote verbatim.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symptomsYesFree-text description of the failure data or context — e.g. 'manufacturing burn-in', 'bearing wearout', 'cosmic-ray bit flips', 'multi-stage degradation'. Substring-matched against a keyword dictionary; returns an ordered shortlist with rationale.bearing wearout

TDQS

A4.7/5.0
Behavior5/5

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

Despite rich annotations (readOnly, idempotent, non-destructive), the description adds significant behavioral detail: the algorithm is deterministic substring matching against a curated dictionary, rationales are from 'ChiAha content', and the output is explicitly not a fit. This goes well beyond annotation hints.

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

Conciseness5/5

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

The description is concise and well-structured, front-loading the main function and then covering algorithm, output caveats, and anti-fabrication in a logical flow. Every sentence adds value.

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?

With one parameter and no output schema, the description fully covers what the tool does, how it works, what to expect from the output, and important limitations. It is complete for a tool of this complexity.

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?

Schema coverage is 100% with a detailed description of the 'symptoms' parameter, so the baseline is 3. The tool description adds extra examples and clarifies the substring-matching behavior, which enriches understanding beyond the schema.

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 action ('return an ordered shortlist of distribution candidates') and the specific input (free-text symptom description). It distinguishes itself from sibling explanation tools by focusing on recommendation rather than conceptual explanation.

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?

The description provides clear context: input symptom text yields distribution candidates, and the output should be treated as a starting point, not a fit. It mentions that actual fitting happens elsewhere, but doesn't explicitly list when NOT to use this tool or name alternative tools for other purposes.

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.

Resources