Route & Logistics Optimizer

Route Optimizer — Get Engine Script (large days)

route_optimizer_get_engine

Read-only

For more than 300 stops, or any order/stop file already in your code sandbox. Returns a complete numpy-only Python script with the same engine as route_optimizer; it reads the stop (and optional vehicle / item / worker / job) files, prints the decision cards and a line per vehicle, and writes the full plan to route_plan.json (and route_plan.geojson). Pass depot, time_limit_seconds and other inputs in params. No stop data reaches this server on this path.

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": "route_optimizer_get_engine"
  }
}

Input Schema

Name	Required	Description
`params`	No	Other inputs exactly as route_optimizer takes them (depot, time_limit_seconds, cost_per_km, road_factor ...).
`jobs_path`	No	Optional jobs file for assignment.
`items_path`	No	Optional items file for load packing.
`stops_path`	No	Stops/orders file in your sandbox (CSV/TSV/JSON).
`workers_path`	No	Optional workers file for assignment.
`vehicles_path`	No	Optional vehicles file.

Output Schema

Name	Required	Description	Default
`notes`	No
`paths`	No
`script`	Yes
`language`	No
`requires`	No
`instructions`	Yes
`writes_files`	No
`engine_version`	Yes
`scipy_required`	No
`data_requirements`	No
`network_access_required`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the readOnly/openWorld annotations, the description discloses exactly what the script does at runtime: reads stop/vehicle/item/worker/job files, prints decision cards plus a per-vehicle line, and writes route_plan.json and route_plan.geojson. It also states the important data-residency property that no stop data reaches the server on this path.

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?

Two sentences, front-loaded with the deciding condition, then the behavior. Dense but every clause carries information (engine parity, outputs, side effects); no filler sentences.

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

Completeness5/5

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

For a script-generating tool with an output schema and full annotation coverage, the description supplies the missing operational context: sandbox file prerequisites, the artifacts produced, and that execution happens client-side. An agent has enough to decide and invoke correctly.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning the schema cannot: params is a passthrough mirror of route_optimizer's inputs (depot, time_limit_seconds, cost_per_km, road_factor). The path parameters are left to their schema descriptions, which is acceptable given full coverage.

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?

States a specific verb and artifact: returns a complete numpy-only Python script using the same engine as route_optimizer. The scope qualifier ('more than 300 stops, or any order/stop file already in your code sandbox') clearly separates it from the sibling route_optimizer tool.

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?

Gives an explicit selection condition (over 300 stops, or files already in the sandbox), which is more than most tools offer. It stops short of stating the inverse ('use route_optimizer for smaller jobs submitted directly'), leaving that routing inference to the agent.

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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