Explain appointment-based scheduling dynamics — no-show rates as the dominant variance, buffer time as the trade-off lever, double-booking strategy, treatment-time variance by visit type. Use for primary care, specialty clinics, dental, or any scheduled-arrival setting.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds topical scope but no additional behavioral traits such as output format or response length. This aligns with annotations but provides minimal extra transparency beyond what annotations already convey.
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, front-loaded with the core purpose and followed by usage guidance. Every clause earns its place; there is no redundant or vague language.
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 explain tool with no parameters and no output schema, the description fully specifies what it explains and when to use it, and it differentiates from sibling tools. No additional context appears necessary for correct selection and invocation.
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?
The tool has zero parameters and the schema is empty, so the description cannot add parameter-level meaning. Per the rubric, a 0-parameter tool receives a baseline of 4, and no further semantic explanation is needed.
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 uses a specific verb ('Explain') and resource ('appointment-based scheduling dynamics'), then enumerates concrete topics (no-show rates, buffer time, double-booking, treatment-time variance). It clearly distinguishes itself from sibling tools by specifying it applies to scheduled-arrival settings, contrasting with ED and walk-in clinics.
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 explicit use cases: 'Use for primary care, specialty clinics, dental, or any scheduled-arrival setting.' This tells the agent when to select this tool, but it does not explicitly name alternative tools or say when not to use it, so it falls short of a 5.
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: listing/describing facility types, explaining specific care settings, running a simulation, or recommending MD counts. The three explain tools are differentiated by their target setting (ED, walk-in, appointment office), and the describe/list pair is distinct from the explain tools.
All tool names follow a consistent verb_noun snake_case pattern: list, describe, explain, simulate, recommend. The objects are specific and clearly named (facility_types, ed_queueing, walk_in_clinic, md_count, ed_demo). No mixed conventions or vague verbs.
Seven tools is well-scoped for a healthcare simulation and teaching server. Each tool has a unique role, with no redundancy. The count is within the ideal 3-15 range and matches the server's focused purpose.
The tool surface is complete for its stated demo scope: users can discover facility types, get conceptual explanations, run simulations, and receive staffing recommendations. The only absent feature (mixed-provider staffing) is explicitly flagged as part of the paid product, so there is no unmet expectation within the free tool's domain.
