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LogiSwarm AI MCP Server

LogiSwarm AI MCP Server

by devcrazy2812
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

optimize_cvrp_routes

Optimizes capacitated vehicle routes using multi-agent planning that accounts for weight, volume, deadlines, and EV priority.

Instructions

Executes multi-agent Capacitated Vehicle Routing optimization taking into account weight, volume, deadlines, and EV prioritization.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "optimize_cvrp_routes"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

B3.1/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, and destructiveHint=false, so the safety profile is partly covered. The description adds only the optimization factors and discloses nothing about side effects, whether it needs prior data, or how it mutates state; for a zero-input 'execute' tool this is thin.

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 states the action and its scope with no filler. It is appropriately sized, though it stops short of adding the routing context that would make it fully self-sufficient.

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 zero-parameter trigger tool with no output schema, the description conveys what gets optimized but omits what input/state it operates on and what the result is. It is minimally adequate but leaves the agent guessing about execution context.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a parameterless tool is 4. The description's mention of weight/volume/deadlines does not map to any input fields.

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 verb ('Executes') and resource ('Capacitated Vehicle Routing optimization') and enumerates the optimization factors (weight, volume, deadlines, EV prioritization), so the purpose is clear. However, it does not differentiate itself from siblings like get_fleet_status or inject_disruption.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus the siblings, no prerequisites, and no exclusions. The agent is left to infer that this is a run/solve action with no indication of what triggers it or what state it expects.

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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