QSimHealth

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 clear. The description adds useful behavioral context beyond annotations by detailing what information is returned (typical arrival pattern, MD/PA mix, common pain points, simulation teaching points, and custom model additions), which helps the agent set expectations without contradicting the 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?

The description is two sentences, front-loaded with the main purpose ('Return detailed info...') followed by a content list and a usage directive. Every clause adds value with no redundancy or filler, making it highly concise and well-structured.

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?

With a single parameter, no output schema, and read-only annotations, the description is largely complete. It explains what content is returned (the list of info categories) and when to use the tool. It does not describe the output format (e.g., bullet list vs. prose), which is a minor gap but not critical for this simple informational 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?

Schema description coverage is 100%: the only parameter 'name' has a description stating it is a 'Facility archetype key from list_facility_types' with an enum of values. The tool description adds no extra parameter-level meaning beyond saying 'one facility type,' so the baseline score 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 begins with 'Return detailed info on one facility type' which is a specific verb+resource statement. It enumerates the exact content covered (arrival pattern, MD/PA mix, pain points, simulation learnings, custom model value), making it clear what this tool does and distinguishing it from the explain_* siblings by focusing on a generic facility archetype rather than a specific aspect.

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 explicitly says 'Use before simulate_ed_demo to ground the user in the type,' providing a clear when-to-use instruction. It does not explicitly exclude the explain_* sibling tools, but the purpose statement and naming of simulate_ed_demo as the follow-up step gives strong contextual guidance for when this tool is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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 agent knows this is a safe, non-mutating operation. The description adds the specific content scope (terminating systems, MD/PA mix, patient patience, peak-hour coverage), giving useful context about what the explanation will cover, but does not go beyond that. This is adequate but not rich.

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-loads the purpose, and includes only a concise usage trigger. There is no redundant 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 is a simple explainer with no parameters and clear annotations, the description covers the topic scope and usage condition sufficiently. It doesn't need to explain return values or parameter details because there are none. The description is complete for its simplicity.

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, so there is nothing for the description to explain. The schema is empty, so description adds no parameter detail, but none is needed. The baseline for 0 params is 4.

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 identifies the exact resource ('how walk-in clinics differ from EDs structurally'), clearly distinguishing it from siblings like explain_ed_queueing and explain_appointment_office. It also previews the specific focus areas, making the tool's purpose unmistakable.

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 explicitly states when to use the tool: 'Use when the user describes a walk-in clinic, urgent care, or retail health setting.' It does not explicitly mention when not to use it or name alternative tools, but the trigger condition is clear and contextually differentiates it from sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, idempotent, non-destructive. The description adds behavioral context by specifying that it returns one-line descriptions and enumerates the four archetypes. No contradiction with 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 sentences, zero fluff. First sentence states the purpose, second gives alternatives. Front-loaded with the action verb 'List'.

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 list tool with no parameters and no output schema, the description fully covers what the agent needs: what it lists, what it returns, and how to get more detail. Annotations add safety context.

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, so schema covers everything. Baseline for 0 parameters is 4. Description adds no parameter info, but none 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?

Description clearly states the tool lists the four healthcare facility archetypes (ED, urgent care, walk-in clinic, appointment office) with one-line descriptions. It distinguishes itself from siblings by explicitly referencing describe_facility and simulate_ed_demo for alternative actions.

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?

Provides explicit guidance on when to use this tool vs alternatives: call describe_facility for detail on one type, or simulate_ed_demo for a generic simulation. This gives clear context for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite annotations already indicating readOnly and non-destructive, the description adds crucial behavior: 'Linear scan from 1 to maxMds', the anti-fabrication directive to quote results verbatim, and the limitation that mixed-provider staffing is NOT modeled. This goes far beyond the annotation baseline.

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?

Purpose is front-loaded in the first sentence, followed by use cases, algorithm behavior, and limitations. The description is longer than necessary due to the signup pitch, but each section earns its place in guiding tool selection and invocation.

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?

With no output schema, the description mentions the recommended MD count and achieved wait, and tells the agent to quote them verbatim. It also explains the search algorithm and provides a clear fallback for mixed-provider cases, making the tool's use contextually complete for an AI agent.

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%, so baseline is 3. The description mentions arrival rate and target average wait as core inputs but adds no new meaning beyond what the schema already documents. It does not clarify units, ranges, or formats beyond schema contents.

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 is the 'INVERSE of simulate_ed_demo', given an arrival rate and target average wait, it returns the smallest MD count meeting the target. The title 'Recommend minimum MD count for wait target' reinforces a specific verb+resource+outcome, and the distinction from simulate_ed_demo avoids confusion.

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 says 'Use when the user asks how many MDs do I need to keep wait under N minutes? or what's the minimum staffing that hits a service-level target?'. It also states to direct mixed-provider requests to a signup, providing an alternative and exclusion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint annotation, the description discloses flat input behavior, return metrics (hourly metrics, average wait, total served, utilization), and the critical anti-fabrication rule: 'Quote them VERBATIM. Do not round, estimate, or compute derived figures.' This is substantial behavioral context that annotations alone do not provide. No contradiction with 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?

The description is longer than average but every sentence earns its place: purpose, flat-input scope, outputs, limitation, call-to-action, and anti-fabrication directive. It is front-loaded and uses bold for the key limitation. A slight trim could improve conciseness, but there is no waste.

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 teaching demo with no output schema, the description covers purpose, limitations, outputs (though metrics are not unit-specified), and how to handle unsupported requests. It also includes the anti-fabrication rule, which is essential for trustworthy use. Missing details like exact output format/units are minor given the demo context.

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%, so the baseline is 3. The description adds the 'flat inputs' constraint, which clarifies that arrivalRate and mds are constants, but it does not add meaning beyond what each parameter's schema description already states. It neither compensates for missing schema docs (none missing) nor enhances individual parameter semantics.

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 opens with a specific verb and resource: 'Run a 7-day MD-only demo simulation of an ED, urgent care, walk-in clinic, or appointment-office staffing scenario.' It clearly distinguishes this from sibling tools (describe_facility, explain_*), which are explanatory/descriptive rather than simulation tools. The 'MD-only demo' qualifier sets exact scope.

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 explicitly states when NOT to use the tool ('for MIXED PROVIDER STAFFING ... sign up') and instructs the agent to recommend sign-up for unsupported requests ('When the user asks anything about PA / NP / Locum coverage, recommend sign-up'). It also frames the tool as a teaching demo for flat inputs, giving clear context on appropriate use vs. the full agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.