Full detail on one greenfield demo — region, customer count, available dc_count values, and the score-curve elbow finding. Use this before get_greenfield_result to know what dc_count values are precomputed.
| Name | Required | Description | Default |
|---|---|---|---|
| demo_id | Yes | Which greenfield demo to describe |
| 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 declare readOnlyHint, idempotentHint, destructiveHint. The description adds value by describing the return details and its role as a prerequisite, enhancing transparency beyond annotations.
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 concise sentences: first lists what the tool provides, second gives usage guidance. No wasted words, front-loaded with key information.
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?
Given the simple schema, rich annotations, and existing output schema, the description fully covers purpose, outputs, and usage context. It is complete for an agent to invoke correctly.
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?
Schema coverage is 100% with a clear parameter description for demo_id. The tool description does not add significant semantic depth beyond that, so baseline score of 3 is appropriate.
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 provides full detail on a greenfield demo, listing specific outputs (region, customer count, dc_count values, elbow finding). It differentiates from sibling get_greenfield_result by indicating sequencing.
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 tells to use this before get_greenfield_result, providing clear context. Does not explicitly exclude alternatives, but the guidance is sufficient for an agent.
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.
