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leomengineer

clinic-mcp-server

by leomengineer

Create appointment (pending approval)

create_appointment

Propose an appointment request that requires human approval before confirmation.

Instructions

Propose a new appointment — does NOT confirm it.

Inserts a row into appointment_requests with status=pending and returns
pending_approval. A human must approve before anything lands on the
confirmed appointments calendar. Calling this twice creates two pending
requests; it never silently books.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
serviceYesService to book, e.g. 'Crown consult'.
datetimeYesProposed start time (ISO-8601). Prefer timezone-aware values; naive values use the clinic timezone (America/Los_Angeles).
patient_idYesExisting patient id, e.g. 'jordan-lee'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusNopending_approval
messageNoAppointment request created and awaiting human approval. Nothing has been committed to the confirmed appointments calendar.
requestYes
Behavior5/5

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

The description adds significant behavioral context beyond annotations: it inserts a row with status=pending, returns pending_approval, requires human approval, and never silently books. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise, uses a short paragraph with a clear first sentence, and each sentence adds unique value. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (3 required parameters, approval workflow), the description covers the behavior, return value, side effects, and constraints. The output schema is implied, and the description is sufficient for an AI agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage with clear parameter descriptions. The tool description does not add further semantics for the parameters, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'propose' and resource 'appointment', clearly distinguishing it from sibling tools like list_appointments which show confirmed ones. It states what it does (inserts a pending request) and what it does not do (confirm).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use it (to propose an appointment requiring approval) and notes that calling it twice creates two pending requests. It implies not for confirmed appointments but does not explicitly state when not to use or name alternatives like list_appointments for checking confirmed ones.

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

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