Fetch complete details for a specific vacation rental property by its lilo code (e.g. PROP-2343). Returns full property data including title, description as markdown text, photos, reviews, amenities, trust score, and booking URL. Optimized for AI deep research consumption.
| Name | Required | Description | Default |
|---|---|---|---|
| lilo_code | Yes | The property lilo code (e.g. PROP-2343) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond the readOnlyHint annotation by listing the specific data returned (title, markdown description, photos, reviews, amenities, trust score, booking URL) and noting the optimization for AI deep research. This helps the agent understand what to expect, though it does not cover error handling or response format details.
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-loads the action, and every sentence provides useful information without redundancy. The return fields are efficiently listed.
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?
Since there is no output schema, the description adequately enumerates the main returned fields (title, description, photos, reviews, amenities, trust score, booking URL) and provides context for its intended use (AI deep research). It does not include details about potential errors or edge cases, but for a simple fetch operation with clear parameters, this is sufficient.
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 only parameter (lilo_code) is fully described in the schema with an example. The description repeats the same example and adds no additional semantic meaning beyond what the schema already provides, so with 100% schema coverage, the baseline of 3 is appropriate.
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 clearly identifies the resource (vacation rental property) and the verb (Fetch), with a specific identifier (lilo code) and return contents. However, it does not distinguish itself from the sibling tool 'get_vacation_rental_details' which has nearly identical name and likely similar function.
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 phrase 'Optimized for AI deep research consumption' implies a use case, but there is no explicit statement about when to use this tool over alternatives, nor any exclusions or alternatives mentioned. The guidance is implied rather than explicit.
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.
