Predict the probability of a chargeback (payment dispute) for a vacation rental booking. Returns risk score, key risk factors, and specific prevention recommendations. Pass booking_id (UUID), optional amount in cents, and optional guest_profile.
| Name | Required | Description | Default |
|---|---|---|---|
| amount | No | Booking amount in cents | |
| booking_id | Yes | Booking UUID | |
| guest_profile | No | Guest profile data |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context by specifying that the tool returns a risk score, key risk factors, and prevention recommendations, and notes that optional inputs (amount, guest_profile) influence the prediction. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary action and outcome. Every word earns its place, and it is appropriately sized for the tool's complexity.
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 prediction tool with three parameters (one nested) and no output schema, the description adequately covers inputs and outputs. It does not detail edge cases or guest_profile structure, but the schema covers parameter names and the description explains the return value well enough.
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 already provides descriptions for all three parameters (100% coverage). The description briefly restates booking_id as UUID, amount as cents, and guest_profile, but does not add significant meaning beyond the schema. Baseline 3 applies because the schema does the heavy lifting.
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 the specific verb 'predict' and names the exact resource: chargeback probability for a vacation rental booking. It clearly differentiates from siblings like assess_vacation_rental_booking_risk and get_chargeback_defense_for_booking by focusing on probability scoring and prevention recommendations.
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 clearly implies when to use the tool (when chargeback probability for a booking is needed) and lists the required/optional inputs. It does not explicitly name alternatives or exclusion conditions, but the context is sufficiently clear for an agent to infer appropriate usage.
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.
There is significant overlap and redundancy among tools, causing ambiguity. For example, analyze_booking_threat_risk, analyze_guest_communication_risk, and analyze_guest_interaction_risk are aliases or near-duplicates, and multiple tools like get_vacation_rental_details and fetch_vacation_rental_details appear to serve the same purpose. This makes it difficult for an agent to choose the correct tool without confusion.
Most tools follow a consistent verb_noun pattern (e.g., check_vacation_rental_availability_and_pricing, get_vacation_rental_details), which aids readability. However, there are minor deviations, such as some tools using 'analyze' vs. 'assess' or 'detect' for similar risk functions, and a few tools like 'ingest_philadelphia_public_records' use a different verb style, but overall the naming is largely predictable.
With 67 tools, the count is excessive for a vacation rental server, leading to bloat and potential overwhelm. Many tools could be consolidated (e.g., multiple risk analysis tools, duplicate property detail fetchers), and the scope feels unfocused, including niche tools like those for Philadelphia events or World Cup compliance that might be better handled as parameters in broader tools.
The tool set provides comprehensive coverage for the vacation rental domain, including booking, risk assessment, compliance, maintenance, evidence management, and guest interactions. There are no obvious gaps; it supports full CRUD/lifecycle operations and specialized workflows, ensuring agents can handle most scenarios without dead ends.
