QueueSim

simulate_schedule

Read-only

Run a queueing simulation against an arbitrary 24-hour staffing schedule. Take this when the user describes a custom day shape that doesn't match a preset (e.g., 'my coffee shop is open 6am–10pm with 4 baristas off-peak, 7 at the 8am rush, 5 at the 4pm rush'). Inputs: arrivalRates (24-element array of customers/hr per hour-of-day) and staffing (24-element array of servers/hr); optional uniform serviceTimeMinutes. Use 0 in both arrays for closed hours (terminating system). Returns the same per-hour metrics + summary shape as simulate_mmc / simulate_scenario. Stronger fit than simulate_scenario when the user's shape doesn't match the four presets; stronger fit than simulate_mmc when they need per-hour variation. ANTI-FABRICATION: numbers come from a real DES run. Quote them VERBATIM. Do not round, estimate, or derive from training-data recall.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`staffing`	Yes	24 hourly staffing counts (servers/hr, one per hour-of-day 0-23). Use 0 for closed hours. Range 0-50 per hour.
`serviceCoV`	No	CoV for service time when distribution is Normal or LogNormal. Range 0-5.
`arrivalRates`	Yes	24 hourly arrival rates (customers/hr, one per hour-of-day 0-23). Use 0 for closed hours (terminating system). Range 0-200 per hour.
`simulationDays`	No	Days to simulate. Range 1-30.
`serviceTimeMinutes`	No	Mean service time in minutes per customer. Applies uniformly across hours. Range 0.1-180.
`arrivalDistribution`	No		Exponential
`serviceDistribution`	No		Exponential

Output Schema

TableJSON Schema

Name	Required	Description
`hourly`	Yes
`inputs`	Yes	Echo of the run's parameters.
`summary`	Yes

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already indicate readOnly and non-destructive, but the description adds critical behavioral context: numbers come from a real DES run and must be quoted verbatim without rounding or estimation. It also explains the terminating-system effect of using 0, which is not in the annotations.

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 a moderately long paragraph, but it is front-loaded with the purpose and every sentence serves a role: example, inputs, closed-hours rule, return shape, alternatives, and anti-fabrication. Slightly dense but not wasteful.

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?

An output schema exists, so return details need not be spelled out. The description provides usage context, alternatives, behavioral constraints, and the per-hour variation emphasis, making it complete for the tool's 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?

Schema coverage is 71%, and the description mostly restates schema descriptions for arrivalRates, staffing, and serviceTimeMinutes. It adds little beyond 'Use 0 in both arrays for closed hours,' which the schema already mentions. Distribution parameters are left to 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 opens with a specific verb+resource: 'Run a queueing simulation against an arbitrary 24-hour staffing schedule.' It clearly distinguishes the tool from siblings by emphasizing custom day shapes and explicitly comparing to simulate_scenario and simulate_mmc.

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 gives an explicit trigger: 'Take this when the user describes a custom day shape that doesn't match a preset,' with a concrete example. It also names alternatives and indicates when each is a stronger fit, plus the closed-hours convention.

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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