Full per-demo write-up: history, what it teaches, what to expect from the run_* output. Use this to ground the user before triggering a sim run, or to explain WHY the demo exists when the user asks a conceptual question about it.
| Name | Required | Description | Default |
|---|---|---|---|
| demo | Yes | Which DRS demo to act on. See list_drs_demos for the catalog. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by specifying the content of the write-up (history, teachings, expected output), which explains what the user gets. This goes beyond the minimal annotation coverage.
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 two sentences with no filler. The first sentence front-loads the core purpose and content, the second gives usage context. Every word earns its place.
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 read-only tool with one enum parameter, the description is complete: it explains what the tool returns, when to use it, and implicitly how it fits among siblings (run_*, explain_*, list_drs_demos). The rich annotations and full schema coverage leave no significant gaps.
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 description coverage is 100% for the single 'demo' parameter, including the enum and a pointer to list_drs_demos. The tool description does not add additional parameter-level semantics, so it stays at the baseline for well-documented schemas.
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 explicitly states it provides a 'full per-demo write-up' covering history, teachings, and expected run_* output, which clearly distinguishes it from sibling explain_* tools (conceptual) and run_* tools (simulation execution). The verb 'describe' matches both the tool name and the resource (demo).
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 gives direct usage guidance: 'Use this to ground the user before triggering a sim run, or to explain WHY the demo exists when the user asks a conceptual question about it.' This implies the contrast with run_* (for executing) and explain_* (for general concepts), and the schema's reference to list_drs_demos covers catalog lookup. No exclusions are needed.
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.
