Exact task selection under a time budget

What does a real call return?

The solver selected Prepare analysis and Run checks: value 150 in 90 of 120 minutes, reported as optimal for these inputs.

Recorded on 2 November 2026 through the public MCP endpoint, service release 0.17.1. Inputs and full responses, response SHA-256 00c5f56d2afe…. Verdict values are the literal values the tool returns. Run the same inputs yourself to compare; elapsed times vary, and a new release may change the figures.

Checked against

• A bounded 0/1 knapsack example whose optimum beats greedy selection, plus a valid empty selection when no task fits.

Recorded on release 0.16.1. These checks cover the listed cases, not every input. Evidence cards.

How it works

This is a bounded 0/1 knapsack problem: each task can be selected once. Dynamic programming searches the value achievable at each integer budget. The result is optimal for the supplied values and constraints.

Inputs and units

Provide a budget of 1 to 1,440 minutes and up to 32 tasks, each with a name, integer duration and non-negative value.

What the result contains

The selected tasks, their total duration and total value can be passed directly to the schedule simulator. Assistants can call both tools independently; the optional native mission composer validates this handoff internally and compares bounded candidate selections.

Example MCP call

{
  "name": "optimize_tasks",
  "arguments": {
    "budget": 120,
    "items": [
      {
        "name": "Prepare analysis",
        "duration": 60,
        "value": 90
      },
      {
        "name": "Run checks",
        "duration": 30,
        "value": 60
      },
      {
        "name": "Write documentation",
        "duration": 45,
        "value": 50
      }
    ]
  }
}

Send this tool name and arguments through a connected MCP client. Discover the authoritative input schema with tools/list.

Call this engine in one command

Node.js 20+ and npm. This downloads a small remote MCP client; it does not install the engine locally.

npx --yes --package https://scorecompute.com/downloads/scorecompute-call-0.14.2.tgz scorecompute-call optimize_tasks --arguments '{"budget":120,"items":[{"name":"Prepare analysis","duration":60,"value":90},{"name":"Run checks","duration":30,"value":60},{"name":"Write documentation","duration":45,"value":50}]}'

Client download, PowerShell input and verification →

Cite this engine

Engine identified by source hash, source SHA-256 3177d5dc10ce…. Evidence recorded on release 0.16.1; the public service currently runs 0.17.1. Replace the access date when you cite it.

ScoreCompute. Exact task selection under a time budget (optimize_tasks), identified by source hash, source SHA-256 3177d5dc10ce36fd14df19e188d075c0e8b9659f907c728fade2154cc3205f16. Evidence release 0.16.1, service release 0.17.1. https://scorecompute.com/engines/optimize-tasks/ (accessed YYYY-MM-DD).

All engine citations · Evidence card

Execution and availability

ScoreCompute exposes this tool through MCP Streamable HTTP. Rust computation runs on a connected worker; the public website and MCP gateway run separately. Among these 22 scientific tools, CUDA is implemented for simulate_pi; the other scientific engines run on CPU. The separate contributor network runs two compiled public workloads: a π estimate and fixed synthetic schedule probes. Requests are bounded and concurrent work may be refused when capacity is occupied.

Record inputs, assumptions and returned provenance when sharing a result. The public observatory displays software client names and tool activity, without publishing calculation arguments or results.