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Plant Simulation Research Bridge

Plant Simulation Research Bridge

by AmaneSuzuha000
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

reproduce_run

Rerun a simulation using the same instance, policy, and seed to verify identical schedules and metrics across replications, exposing nondeterminism in scheduling experiments.

Instructions

Run the same instance/policy/seed several times and verify identical schedules and metrics.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "instance": "<instance>",
      "policy": "<policy>"
    },
    "name": "reproduce_run"
  }
}

Input Schema

NameRequiredDescriptionDefault
seedNo
jitterNo
policyYes
instanceYes
work_dirNo
replicationsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It discloses the core behavior (repeated runs plus equality checking of schedules and metrics), but omits what happens on mismatch, whether results are persisted, and cost/runtime implications.

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?

A single dense sentence with no wasted wording; the core action and verification goal are front-loaded.

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

Completeness2/5

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

For a six-parameter tool with no annotations, no output schema, and no schema parameter descriptions, the one-line description is not complete enough. It leaves failure behavior, output shape, and three parameters undocumented.

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

Parameters2/5

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

Schema description coverage is 0% across six parameters. The description only implies the roles of instance, policy, and seed, and says nothing about jitter, work_dir, or replications, leaving half the inputs unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific action: run the same instance/policy/seed multiple times and verify identical schedules and metrics. This distinguishes it from a generic experiment run, though it does not explicitly contrast with siblings like schedule_vs_reference or compare_policies.

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

Usage Guidelines3/5

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

The verification-of-identical-results framing implies when to use it, but there is no explicit guidance on when not to use it or which sibling to choose for related comparison tasks.

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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