ReliaStats

list_paired_models

Read-onlyIdempotent

Return the catalog of paired models — concrete real-world systems that live in two ChiAha sandboxes simultaneously, one for dynamics (DES via ReliaSim) and one for statistics (distribution fitting + validation via ReliaStats). Today: a single paired model — the bottling line. Returns canonical model IDs + cross-MCP routing metadata (which ReliaSim chapter, which ReliaSim MCP tools, which ReliaStats mode consumes which file shape). Use when a user asks about cross-MCP workflows, paired sandboxes, or the bottling-line example. ANTI-FABRICATION: this is a soft-reference catalog — to actually run a simulation, the LLM client calls ReliaSim's MCP tools directly.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnly, idempotent, non-destructive. The description adds current catalog size ('Today: a single paired model'), return content (canonical IDs + routing metadata), and the anti-fabrication caveat about not actually running simulations.

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?

Five sentences, each adding distinct value: purpose, current state, returns, usage, and anti-fabrication. No fluff or repetition.

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?

Despite no output schema, the description clearly states what is returned (canonical model IDs + cross-MCP routing metadata) and the current content. It includes usage guidance and the soft-reference caveat, making it sufficient for an agent.

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 schema has zero parameters, so the description doesn't need to explain parameter usage. Baseline 4 applies; the description adds no parameter-related detail, which is acceptable.

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 opens with 'Return the catalog of paired models' — a specific verb and resource. It further clarifies the scope (dynamics + statistics sandboxes) and differentiates from siblings by emphasizing cross-MCP routing metadata.

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

Usage Guidelines5/5

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

It explicitly states 'Use when a user asks about cross-MCP workflows, paired sandboxes, or the bottling-line example.' The anti-fabrication note adds a clear boundary: for actual simulations, call ReliaSim tools directly, effectively naming the alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All ReliaStats tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?