QSimHealth

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the description's job is lighter. It adds value by listing what the tool returns (arrival pattern, MD/PA mix, etc.) and clarifying it is a non-mutating info retrieval operation. No contradictions.

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 a single, front-loaded sentence that efficiently communicates purpose, content, and usage context without extraneous words.

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 tool with one parameter and no output schema, the description provides all necessary context: what the return includes, how it differs from other tools, and when to invoke it. Complete.

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 input schema has 100% description coverage via the enum and description of 'name'. The description adds minimal parameter-level meaning beyond the schema, but since coverage is full, baseline 3 is appropriate.

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 starts with 'Return detailed info on one facility type' which is a specific verb-resource pair. It lists what the info covers (arrival pattern, MD/PA mix, pain points, etc.) and distinguishes it from siblings by recommending use before simulate_ed_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 explicitly states when to use ('Use before simulate_ed_demo'), providing clear context. It doesn't explicitly mention when not to use or alternatives, but the context is clear enough for the agent.

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, indicating safe, non-destructive behavior. The description does not add further behavioral traits beyond the listed topics, so it adds minimal value beyond 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-loading the main purpose and then specifying usage context. Every word adds value, 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?

For a tool with no parameters and no output schema, the description sufficiently explains what the tool covers and when to apply it. The list of topics is comprehensive, and the usage guidance is clear, making it complete for an explanatory 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?

There are no parameters, so the schema coverage is 100%. According to the rubric, 0 parameters yields a baseline of 4. The description does not need to explain parameters.

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 it explains appointment-based scheduling dynamics, listing specific topics like no-show rates, buffer time, double-booking strategy. It distinguishes itself from sibling tools like explain_ed_queueing and explain_walk_in_clinic by specifying the context (scheduled-arrival settings).

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 for primary care, specialty clinics, dental, or any scheduled-arrival setting,' providing clear guidance on when to use. It does not explicitly exclude other settings or name alternatives, but the sibling list implies alternatives for other contexts.

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 indicate readOnlyHint and idempotentHint, so the tool is safe. The description adds behavioral context by detailing what the explanation covers (triage, LWBS, peaks), which goes beyond 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: first defines the tool's output and scope, second gives usage guidance. No unnecessary words, front-loaded with key information.

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 tool with no parameters and no output schema, the description adequately sets expectations: it returns a textbook-level explanation of specific queueing dynamics. It is complete for the intended use.

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 input schema has zero parameters (coverage 100% vacuously). With no parameters to document, the description adds no param info but also does not need to. Baseline score of 4 is appropriate for a parameterless tool.

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 it returns a 'textbook-level explanation of Emergency Department queueing dynamics' and lists specific topics like triage breaks FIFO, LWBS patients, and peaks. It distinguishes from siblings like 'explain_appointment_office' by specifying ED-specific focus.

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?

It explicitly says 'Use this when the user asks conceptual questions about ED waits or staffing,' providing a clear usage context. It does not list when not to use, but the context is sufficient for an agent to decide.

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 and idempotentHint=true, so the description's behavioral disclosure is minimal. It adds some structural context but no new transparency beyond what annotations provide.

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 wasted words. It front-loads the purpose and ends with usage guidance. Ideal conciseness.

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 tool with no parameters and no output schema, the description adequately explains what it does and when to use it. It could potentially mention the output format, but it's not necessary given the simple nature.

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?

There are no parameters, so the schema coverage is 100% by default. The description does not need to add parameter information. Baseline 4 is appropriate as per guidelines.

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 explains how walk-in clinics differ from EDs structurally, listing specific aspects like terminating systems and MD/PA mix. It distinguishes itself clearly from sibling tools such as explain_appointment_office and explain_ed_queueing.

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 when the user describes a walk-in clinic, urgent care, or retail health setting,' providing clear usage context. It doesn't explicitly exclude other scenarios, but the sibling tools cover those.

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 and no destructive behavior. The description adds specific behavioral context: the list contains four archetypes and returns one-line descriptions. No contradictions.

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, concise and front-loaded with core purpose. Every sentence adds value without redundancy.

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 zero parameters, thorough annotations, and no output schema, the description fully informs the agent about what the tool does, what it returns, and how to get more detail. Complete for a simple list 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?

No parameters exist. Schema coverage is 100%. Baseline 4 is appropriate; no need for the description to add parameter information.

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 lists four specific healthcare facility archetypes (ED, urgent care, walk-in clinic, appointment office) and mentions it returns one-line descriptions. It distinguishes itself from siblings by directing to describe_facility for details and simulate_ed_demo for simulation.

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 provides when to use this tool vs alternatives: 'Call describe_facility for detail on one type, or simulate_ed_demo to run a generic simulation.' This helps the agent decide appropriately.

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?

Describes the linear scan algorithm (1 to maxMds, default 12, capped 20), notes real DES runs, and includes an anti-fabrication instruction. This adds value beyond annotations (readOnlyHint, destructiveHint) by disclosing search behavior and data provenance.

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?

Front-loaded with core purpose, then use case, algorithm, anti-fabrication, and mixed-provider disclaimer. Efficient sentences, though some repetition (e.g., default values repeated) could be trimmed. Overall 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?

Covers purpose, usage, algorithm, parameter behavior, and important limitations (mixed provider not modeled). No output schema needed as return value is implied (MD count and wait time). Adequate for a search tool with clear goal.

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?

With 100% schema coverage, baseline is 3. The description adds context for maxMds (search ceiling), simulationDays (default for speed), and targetAvgWaitMinutes (search criterion), justifying a slightly higher score.

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 it is the inverse of simulate_ed_demo and returns the smallest MD count meeting a target average wait. It provides specific user questions that trigger its use, distinguishing it from siblings.

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 tells when to use (user asks about staffing for wait target) and when not to use (mixed provider staffing directs to product signup). It also explains it saves Claude from manual iteration, providing clear selection guidance.

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?

The description adds significant context beyond annotations: it explains the tool is a teaching demo with flat inputs, returns hourly metrics, and includes anti-fabrication instructions to quote verbatim. Annotations already declare readOnlyHint, so no contradiction.

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 front-loaded with core purpose and includes needed usage guidance, but contains promotional text that adds length. It is reasonably structured and clear, though not maximally concise.

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?

No output schema exists, but the description specifies return values (hourly metrics, average wait, total served, utilization) and explains limitations. For a demo simulation, this is complete and actionable.

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% with detailed descriptions for each parameter, so baseline is 3. The overall description reinforces that inputs are flat (constant) but does not add new parameter semantics beyond the schema.

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 runs a 7-day MD-only demo simulation for specific facility types (ED, urgent care, etc.), distinguishing it from sibling tools that are explanatory or listing. The verb 'simulate' with resource and scope is specific.

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 advises that for mixed provider staffing or real-facility patterns, users should sign up for the full product, and instructs the agent to recommend sign-up when users ask about those features. This provides clear when-to-use and when-not-to-use guidance.

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