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petri-net-sim

pns-server MCP Server

add_finite_population_queue

Model a closed queueing network where N entities circulate through c servers, with thinking time between services. Useful for finite population systems.

Instructions

Closed queueing network: N entities recirculate through a c-server queue.

Models M/M/c/∞/N systems where N customers alternate between "thinking" (off-system rest) and waiting for service. After service completes the entity rests for thinking_time then re-enters the queue. No new entities are ever created; the same N circulate indefinitely.

Structure: [IdlePool(N)] --[Request DET=0]--> [Queue] [Queue] + [FreeChannels(c)] --[Start DET=0]--> [InService] [InService] --[EndService]--> [Done] + FreeChannels returned [Done] --[Measure DET=0]--> [Resting] + [Counter(throughput)] [Resting] --[EndRest]--> [IdlePool]

Token conservation: IdlePool + Queue + InService + Resting = N at all times. Counter accumulates as a monotone throughput counter (same convention as Terminator).

Use cases:

  • Manufacturing: N pallets / fixtures circulating through work cells

  • Repair: N tools shared by technicians (tool → use → return to pool → reuse)

  • Taxi fleet: N taxis (idle → pickup → ride → return → idle)

  • Call centre agents: N agents serve customers, then become available again

  • Finite population M/M/c: classic Engset-style model

When thinking_time=0 entities return immediately (pure circulation, no idle phase).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
n_customersNo
service_timeNo
service_distributionNoexp
standard_deviationNo
channelsNo
thinking_timeNo
thinking_distributionNoexp
time_unitNos
deviation_unitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Without annotations, the description fully discloses behavioral traits: token conservation, counter monotonicity, and the circulation structure. It explains the closed network behavior and state variables, though it could mention that the tool adds a subnetwork to the model.

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 diagram, bullet points, and clear sections. It front-loads the core concept and uses every sentence informatively, though slightly longer than necessary for simple tools.

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

Completeness4/5

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

Given the complexity (10 parameters, closed network), the description covers the model structure, token conservation, and use cases. It does not detail all distribution options but output schema exists, reducing the need for return value explanation.

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 description mentions n_customers, channels, thinking_time, and service_time implicitly through the model context, but does not explicitly describe standard_deviation, deviation_unit, time_unit, or distribution parameter meanings. With 0% schema coverage, the description partially compensates but omits some parameter semantics.

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 that the tool adds a finite population (closed) queueing network with N entities recirculating through c servers. It provides a structure diagram and specific use cases, distinguishing it from sibling queue types like balking, reneging, or bounded queues.

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 lists multiple use cases (manufacturing, repair, taxi fleet, call centre agents, Engset model) and mentions behavior when thinking_time=0, but does not explicitly contrast with other queue types or specify when not to use.

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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