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petri-net-sim

pns-server MCP Server

add_nonstationary_generator

Model arrival processes with time-varying rates using cyclic schedules. Define multiple time windows each with its own arrival rate for realistic simulations of peaks and off-peak periods.

Instructions

Add a NonStationaryGenerator — arrival rate changes over cyclic time windows.

Each time window has its own arrival rate. The schedule repeats cyclically.

Use this for:

  • Morning rush (rate 20/h, 2h) + afternoon (rate 5/h, 6h)

  • Factory: day shift (rate 10/min, 8h) + night shift (rate 3/min, 16h)

  • Call center: peak hours vs off-peak

Args: name: Name for this generator (e.g., "CoffeeShop", "CallCenter") schedule: List of time windows. Each dict: {"duration": float (seconds), "rate": float (arrivals/sec)} Example: [{"duration": 7200, "rate": 0.33}, {"duration": 21600, "rate": 0.083}] batch_size: Tokens per arrival (default: 1)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
scheduleYes
batch_sizeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It explains the cyclic behavior and schedule structure, but does not disclose side effects, creation vs. modification, or what happens with invalid input. The transparency is adequate but not thorough.

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 well-structured with a clear headline, usage examples, and an Args section. It is not overly verbose; the examples are helpful. Could be slightly more concise by omitting some redundancy, but overall efficient.

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

Completeness3/5

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

Given no output schema shown and no description of return value, the description is incomplete. It also does not relate to other generator tools among the many siblings. For a simple add tool, the missing return info is a notable gap, but the core functionality is covered.

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?

With 0% schema coverage, the description adds detailed semantics: name with examples, schedule with explanation and example dictionary format, batch_size with default and meaning (tokens per arrival). It compensates well, though the schedule type remains somewhat vague due to 'additionalProperties: true' in 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 tool adds a NonStationaryGenerator with arrival rate changing over cyclic time windows, distinguishing it from the stationary add_generator among siblings. The verb 'Add' and the resource 'NonStationaryGenerator' are explicitly connected.

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 explicit use cases (morning rush, factory shifts, call center) and explains the cyclic repetition. However, it does not explicitly state when NOT to use this tool or mention the alternative stationary generator, though the examples imply when it's appropriate.

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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