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Decision Optimization

decision_optimization
Destructive

Call immediately before an autonomous system must choose among multiple feasible actions under limited resources, costs, capacities, eligibility rules, and an economic objective. Decision Optimization compiles a typed economic problem — supplier/inventory allocation across sources and demands, task-to-resource assignment, budget-constrained option selection, or production/resource mix — into a bounded MILP model, solves it with a fixed deterministic solver profile, and independently re-verifies the candidate decision against the original problem before returning it. Returns OPTIMAL, FEASIBLE, INFEASIBLE, UNBOUNDED, or NO_SOLUTION with machine-readable evidence: verified feasibility, solver optimality/gap, binding constraints, and stable fingerprints. It does not perform vehicle routing, scheduling, calendar planning, 3D/carton packing, arbitrary LP/MIP solving, natural-language optimization modeling, forecasting, or external data retrieval. Paid invocation: 30000 micro-USD per successful execution.

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
statusYes
qualityYes
decisionNo
evidenceYes
economicsYes
diagnosticsYes
problem_typeYes
machine_actionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior1/5

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

Description contradicts annotations: it states a 'fixed deterministic solver profile' and 'does not perform external data retrieval', while annotations mark openWorldHint=true (open-world/nondeterministic behavior) and destructiveHint=true. It also fails to disclose any destructive side effect, only mentioning paid invocation, whicheaves the destructive hint unexplained.

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?

The description is dense but efficient, front-loading the when-to-use instruction and then explaining the internal process, return values, exclusions, and cost. Every sentence carries information, and the exclusion list is valuable for tool selection. Slightly long but justified by the tool's complexity.

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

Completeness4/5

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

Covers when to call, supported problem types, internal solving and verification behavior, return statuses, explicit exclusions, and pricing. An output schema exists, so return-value details are not needed in the description. The main gap is lack of guidance on choosing among the four anyOf branches, though the schema covers parameter structure.

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?

Context signals report 100% schema description coverage, so the schema already documents parameters. The description adds no per-parameter meaning beyond naming the four problem categories (allocation, assignment, selection, mix), which maps to the anyOf branches but does not elaborate on individual fields. Baseline 3 is appropriate.

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 resource: 'Call immediately before an autonomous system must choose...' and describes the core behavior: compiles typed economic problems into a bounded MILP model, solves with a deterministic solver, and re-verifies the result. Explicitly differentiates from non-goals with an exclusion list (routing, scheduling, packing, arbitrary MILP, etc.), which helps an agent distinguish it from other tools.

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

Usage Guidelines5/5

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

Gives explicit when-to-use: before an autonomous system must choose among feasible actions under limited resources, costs, capacities, eligibility rules, and an economic objective. Lists the four supported problem families and a clear when-not list, including vehicle routing, scheduling, arbitrary LP/MIP solving, and external data retrieval, so the agent knows this tool is not for those cases.

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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