Retrieve the final OutcomeReceipt for a completed operation.
WHEN TO USE: Use after get_status returns success/failure/partial to retrieve the full result with cost and reason codes. WHEN NOT TO USE: Do not use for operations still in pending/executing state — use get_status first. COST: free - no key required LATENCY: ~50ms
| Name | Required | Description | Default |
|---|---|---|---|
| operation_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context beyond annotations: it returns a final OutcomeReceipt containing cost and reason codes, and discloses cost and latency (~50ms). No contradiction with annotations exists.
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 compact, front-loaded with the core purpose, and uses clearly labeled WHEN TO USE/WHEN NOT TO USE sections. Cost and latency lines add concise, decision-relevant information without padding.
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 one-parameter read-only tool with no output schema, the description covers the essential workflow: wait for get_status, then retrieve the final result. It mentions output contents (cost and reason codes), but the absence of an output schema means return-value details are only partially specified.
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%, so the description must compensate. It indirectly clarifies that operation_id refers to a completed operation and that this tool is used after get_status, but it does not explicitly describe where the operation_id comes from or its format. The single self-explanatory parameter keeps this from being lower.
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: 'Retrieve the final OutcomeReceipt for a completed operation.' It also distinguishes itself from get_status by explaining that get_outcome is the follow-up that retrieves the full result with cost and reason codes.
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 provides explicit WHEN TO USE and WHEN NOT TO USE guidance, naming get_status as the prerequisite/alternative and explaining that pending/executing operations should not be queried with this tool. This leaves no ambiguity about invocation timing.
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.
Each tool maps to a distinct stage or concern: link preflight vs import, business discovery vs verification, scheduling, async status/outcome, cost preview, and health. The closest pair (check_booking_link and import_booking_url) is explicitly differentiated as a free guard vs actual registration.
Nearly all tools follow a consistent verb_noun snake_case pattern such as find_business, verify_business, schedule_appointment, and get_status. The only real deviation is self_test, which is a standard health-check name but not verb_noun.
Nine tools is well-scoped for an appointment-booking service that also needs async operation tracking, cost estimation, and health checks. Each tool has a clear role and none feel redundant or tacked on.
The toolset covers the main lifecycle: find or import an SMB, verify it, schedule/cancel/reschedule, poll status, retrieve outcomes, and preview costs. Minor gaps exist—send_message and capture_lead are referenced as downstream operations but not exposed, and there is no direct tool for listing supported platforms.