get_service_optionsUse after intake required fields are known, or to refresh matching data. It returns live aggregated potential windows and the current refinement batch without company names. It does not select a provider or reserve capacity.
| Name | Required | Description | Default |
|---|---|---|---|
| request_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful context: data is live and aggregated, company names are omitted, and no capacity is reserved, which goes beyond the generic safety hints.
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?
Three sentences, each with a distinct job: when to use, what it returns, and what it does not do. No filler or repetition.
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 an output schema, rich annotations, and only one parameter, the description covers usage, behavior, and exclusions well. The only notable gap is not explaining where request_id comes from, but the low complexity and self-describing parameter keep it nearly 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?
Schema coverage is 0% and the description never explains request_id, its origin, or its relationship to the intake flow. The property name is self-explanatory as an ID, but the description adds no parameter-level meaning to compensate for the missing schema documentation.
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 and resource: it returns live aggregated potential windows and the current refinement batch, and explicitly scopes itself by saying it does not select a provider or reserve capacity. This clearly distinguishes it from sibling tools like select_service_option.
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 gives explicit timing: use after intake required fields are known, or to refresh matching data. It also states what the tool does not do (select/reserve), but it does not name the alternative tool to use for those actions.
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.
Most tools map to distinct lifecycle stages: request intake, option selection, booking creation, and booking management. The main potential confusion is between get_service_options and select_service_option, and between prepare_service_booking_change versus the actual cancel/reschedule tools, but per-tool guidance makes the boundaries clear enough for an agent.
All tool names follow a snake_case verb_noun pattern such as start_service_request, update_service_request, get_service_options, and reschedule_service_booking. Minor variations like get_my_service_bookings and prepare_service_booking_change are still natural and predictable.
Twelve tools is well-scoped for a booking and appointment-management domain. Each tool serves a necessary step in the flow: starting a request, updating intake, selecting options, creating bookings, listing/getting bookings, and handling changes.
The lifecycle is largely complete: request creation and updates, option selection, booking creation, read/list, reschedule, and cancelation. Obvious gaps are minor, such as no explicit tool to abandon a service request or retrieve a single service request alone, but the core user journey has no dead ends.