Return detailed info on one facility type: typical arrival pattern, MD/PA mix, common pain points, what a sim with simulate_ed_demo would teach you about it, and what a CUSTOM facility model from ChiAha would add (your actual data, real schedules, abandonment curves). Use before simulate_ed_demo to ground the user in the type.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Facility archetype key from list_facility_types. | ed |
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.
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.
