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QueueSim

describe_scenario

Read-onlyIdempotent

Return full details for one preset scenario: title, description, teaching note, peak parameters, and per-hour arrival + staffing arrays. Use this before simulate_scenario to understand the default shape and what overrides make sense.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesScenario key from list_scenarios.coffee

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYes
titleYes
defaultsYes
descriptionNo
teachingNoteNo
supportedOverridesNo

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds contextual value by specifying the returned fields and that it reveals the 'default shape' for overrides, which is behavior beyond what annotations state. No contradiction exists, so a 4 is appropriate.

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 two sentences, front-loaded with the tool's action and content, followed by a clear usage directive. No redundant words, and each sentence serves a distinct purpose (what it does and when to use it).

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 enum parameter, a rich output schema, and comprehensive annotations, the tool is simple. The description provides all necessary context: what it returns, the specific fields, and how it fits into the workflow (before simulate_scenario). There's no missing information for this level of complexity.

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

Parameters3/5

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

The schema provides 100% coverage for the single parameter 'name' with enum, default, and description. The tool description does not add parameter-specific details beyond the schema's explanation of scenario keys. Following the baseline rule for high schema coverage, a 3 is suitable.

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 begins with 'Return full details for one preset scenario' which is a specific verb+resource, and it enumerates the exact contents (title, description, teaching note, peak parameters, staffing arrays). It also distinguishes itself from the sibling simulate_scenario by advising to use it before simulating, clearly differentiating its purpose.

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?

The description explicitly states when to use the tool: 'Use this before simulate_scenario to understand the default shape and what overrides make sense.' This provides clear context and a direct alternative/pairing with simulate_scenario, meeting the highest bar for usage guidance.

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

A4.7/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: simulation (simulate_mmc, simulate_scenario, simulate_schedule), comparison (compare_analytical_vs_simulated, compare_separate_vs_pooled), inverse analysis (recommend_staffing), interpretation (interpret_result), education (explain_queueing_theory, explain_advanced_patterns), and scenario management (list_scenarios, describe_scenario). No two tools overlap significantly, and nuanced differences are explicitly documented (e.g., when to use simulate_mmc vs simulate_scenario).

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case: compare_*, describe_*, explain_*, interpret_*, list_*, recommend_*, simulate_*. Even compound names like compare_analytical_vs_simulated are clearly structured and match the pattern. There are no mixed conventions or vague verbs.

Tool Count5/5

11 tools is well within the ideal 3-15 range and each tool earns its place. The set covers simulation, comparison, recommendation, interpretation, education, and scenario discovery without redundancy or bloat. The count feels right for a queueing theory teaching and simulation server.

Completeness5/5

The tool surface is complete for its stated domain: it offers multiple simulation modes (generic, preset, custom schedule), an inverse staffing finder, analytical-vs-simulation comparison, pooled-vs-separate comparison, interpretation, and educational explainers. There are no obvious dead ends—users can model, validate, understand, and optimize queueing scenarios. The intentional exclusion of advanced pattern simulation is addressed by explain_advanced_patterns pointing to ChiAha.

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