Run the Hamburger Duo (HAM) demo — Andy Siprelle's 5-stage finite-source line, executed as both DES and DRS implementations on the same model so the event-count and throughput numbers can be compared apples-to-apples. Returns engine output for the side-by-side run. ANTI-FABRICATION: numbers come from a real engine run; quote verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
| simulation_days | No | Days to simulate. Default 7. Range 1-30. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds useful behavioral context beyond the annotations: it returns engine output for the side-by-side run and warns that numbers come from a real engine run and must be quoted verbatim. This complements the readOnly and non-destructive hints without contradicting them.
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?
The description is compact and front-loaded: the first sentence conveys the core purpose, the second states the return value, and the third provides a critical anti-fabrication warning. Every sentence earns its place with no redundancy or filler.
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 tool's simple interface (one optional parameter fully documented in the schema) and the presence of annotations, the description is mostly complete. It states what is run, why, what is returned, and the quoting requirement, though it does not detail the output format in the absence of an output schema.
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% because the sole parameter, simulation_days, has a full description including default, range, and meaning. The tool description adds no parameter-specific information, but the schema carries the entire semantic load, so the baseline of 3 applies.
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 states the specific action ('Run the Hamburger Duo (HAM) demo') and identifies the unique resource: Andy Siprelle's 5-stage finite-source line, with both DES and DRS implementations. It clearly distinguishes this from sibling run_* demos by naming the specific demo and its dual-implementation purpose.
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?
The description implies when to use the tool: when you need to compare event-count and throughput numbers between DES and DRS implementations on the same model. However, it does not explicitly mention alternatives or when not to use it, leaving the usage guidance implicit rather than explicit.
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.
Every tool has a clearly distinct purpose: the explain_* tools each target a different DRS concept, list_drs_demos and describe_demo handle discovery/context, and each run_* tool executes a specific demo. run_showcase is explicitly differentiated as a live experiment generator, so there is no ambiguity.
All tool names follow a consistent verb_noun pattern with lowercase and underscores (explain_*, run_*, list_, describe_). This makes the API predictable and easy to navigate.
With 14 tools, the server sits comfortably in the ideal 3-15 range. The count is well-scoped for its purpose: a mix of educational explainers, demo discovery, and demo execution tools, each earning its place.
The tool surface is comprehensive for the DRS demo domain: users can discover demos (list_drs_demos), get detailed context (describe_demo), learn core concepts (explain_*), run fixed reference demos (run_*), and perform custom experiments (run_showcase). No significant gaps hinder the intended workflows.
