Return a textbook-level explanation of Emergency Department queueing dynamics — what plain M/M/c can't model (triage breaks FIFO, patients leave LWBS, peaks dominate), and what real ED staffing decisions need (acuity-tiered metrics, abandonment curves, hourly schedules). Use this when the user asks conceptual questions about ED waits or staffing.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context by detailing what the explanation covers (limitations of plain M/M/c, triage, LWBS, peaks) and what staffing decisions need. This goes beyond the annotations, though it doesn't specify response format or length, which is acceptable for a zero-parameter explanation tool.
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, front-loaded with the primary purpose, followed by a clear usage directive. Every word earns its place, with no repetition of schema or annotation data. This is a model of concise, well-structured documentation.
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 zero-parameter explanatory tool with rich annotations, the description covers the scope and applicability well. It mentions both the limitations of standard queueing models and the practical needs of ED staffing decisions. A minor gap is not describing the output format (e.g., text length or structure), but this is not critical given the simplicity of the tool.
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 description coverage is 100%. Per guidelines, the baseline for 0 params is 4. The description does not need to add parameter semantics, and it correctly focuses on the explanation content rather than inputs.
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 the tool returns a 'textbook-level explanation of Emergency Department queueing dynamics', which is specific and distinct from sibling tools like explain_appointment_office and explain_walk_in_clinic. It names the exact resource (ED queueing dynamics) and the verb (Return/explain), fully clarifying its 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 provides an explicit usage cue: 'Use this when the user asks conceptual questions about ED waits or staffing.' This gives clear context for when to use the tool, though it does not explicitly state when not to use it or name alternatives. It is sufficient for guiding selection among siblings.
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.
