List the bundled SCModeling sample supply-chain models. Returns a catalog with each model's id and a short description. Use this before run_simulation to know which model_id values are valid.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| content | No | MCP content blocks — single text block with the response body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds return format (catalog with id and description), which is useful but not required beyond annotations. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. Front-loaded with action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no parameters and an output schema, the description covers purpose, return content, and usage context. Complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, so the description does not need to add param info. Baseline for 0 parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists bundled SCModeling sample supply-chain models and returns a catalog with id and description. It differentiates from sibling tools like list_greenfield_demos by specifying it's for sample supply-chain models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use before run_simulation to know valid model_id values. This provides clear context, though it does not state exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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).
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.
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.
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.
