SCModeling

get_opt_result

Read-onlyIdempotent

Get the precomputed result for one scenario of an optimization demo. Returns the verbatim engine output JSON (AMOS for tariff/coffee, SSO output for sso-basic) including the optimal sourcing/production/transport decisions, costs, and any open/close facility variables. ANTI-FABRICATION: every numeric result is verbatim from the optimization engine that ran offline — quote them in your reply, do not round or recompute. Call describe_opt_demo first to learn valid scenario_key formats for each demo.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`demo_id`	Yes	Which optimization demo
`scenario_key`	No	Scenario key within the demo. Format varies per demo — call describe_opt_demo for the exact valid keys before guessing. Tariff uses 'APAC=<N>' where N is one of 0, 7.5, 25, 50, 100. Coffee uses '<configKey>\|DSL=<N>' where configKey is T/TA/TS/TAS and N is 20-70 in steps of 5 (10c units of $/gal). sso-basic is single-scenario; scenario_key is ignored.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`content`	No	MCP content blocks — single text block with the response body

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds anti-fabrication rule (do not round/recompute), verbatim return, and scenario_key behavior for sso-basic. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each dense with information. Front-loaded with purpose. No fluff. Efficient and well-structured.

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?

Given complexity of different demo formats, presence of output schema, and rich annotations, description is fully complete. Covers variations, anti-fabrication, and prerequisite call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds critical context: scenario_key format varies per demo, gives examples, and notes scenario_key is ignored for sso-basic. Provides beyond schema.

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?

Clearly states it gets precomputed result for a scenario of an optimization demo. Specifies it returns verbatim engine output, distinguishing it from sibling tools like describe_opt_demo or get_greenfield_result.

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?

Explicitly advises to call describe_opt_demo first to learn valid scenario_key formats. Provides specific format examples for tariff, coffee, and sso-basic. Lacks explicit 'when not to use' but provides sufficient guidance.

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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TDQS

A4.5/5.0

Disambiguation5/5

Each tool has a clearly distinct role: listing, describing, explaining, getting results, or running simulation. No two tools overlap in purpose; for example, describe_* and explain_* serve different needs (precomputed detail vs. conceptual reference).

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with underscores: describe_, explain_, get_, list_, run_. The verb indicates the action, and the noun indicates the resource, making it predictable and easy to understand.

Tool Count5/5

With 11 tools, the set is well-scoped for a demonstration-focused supply chain modeling server. It covers listing, describing, explaining, retrieving results, and running a simulation without unnecessary duplication or missing essential operations.

Completeness4/5

The tool surface is comprehensive for querying and running bundled demos, but it lacks create/update/delete operations for models or demos. This is acceptable since the server provides precomputed results, but it represents a minor gap in lifecycle coverage.

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