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LogiSwarm AI MCP Server

LogiSwarm AI MCP Server

by devcrazy2812
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

inject_disruption

Destructive

Simulates supply chain disruptions like breakdowns, severe weather, road closures, or urgent surges to trigger self-healing rerouting and test recovery.

Instructions

Simulates a supply chain crisis (breakdown, severe_weather, road_closure, urgent_surge) and triggers self-healing rerouting.

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": {
      "disruption_type": "<disruption_type>"
    },
    "name": "inject_disruption"
  }
}

Input Schema

NameRequiredDescriptionDefault
dry_runNoIf true, simulates disruption impact without mutating live controller state
disruption_typeYesType of disruption to inject

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare destructiveHint=true, idempotentHint=false, readOnlyHint=false, so the safety profile is known. The description adds real value by disclosing the self-healing rerouting side effect, but the word 'Simulates' sits awkwardly against a destructive, non-idempotent mutation of live controller state, and the description never says the change is irreversible or affects the live fleet.

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?

A single front-loaded sentence that leads with the action and the effect. The parenthetical enum list mildly duplicates the schema but is short enough to not be wasteful.

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?

For a destructive mutation tool with no output schema, the description covers the action and side effect but omits what the agent should expect afterward (reroute results, recovery state) and any confirmation that live state is mutated. Adequate but with clear gaps.

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 description coverage is 100%, so both parameters are already documented, including dry_run's 'without mutating live controller state'. The description only repeats the enum values already in the schema and adds nothing about dry_run or default behavior; baseline 3 applies.

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?

Names a specific verb and resource ('Simulates a supply chain crisis') and enumerates the four disruption types, so the agent knows exactly what gets injected. It also states the downstream effect ('triggers self-healing rerouting'). It does not explicitly differentiate itself from the read/optimize siblings, which is the only gap.

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 scenario described implies usage (exercise resilience by injecting a crisis), so the intent is inferable. However, there is no explicit when-to-use, no when-not-to-use, and no mention of alternatives such as evaluate_feedback_loop for observing recovery.

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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