get_solution_detailGet full detail for a Tuki solution: description, who it is for, capabilities, status and contact / CTA. Use after list_solutions or when the user asks about a specific Tuki product (WhatsApp Booking OS, boutique ticketing, rental inventory software, event post-sale, tailor-made tourism software).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Solution slug (e.g. "whatsapp-booking-os"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds transparent value by enumerating the exact fields returned, which helps the agent know what to expect. It does not introduce any contradictions with the 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?
The description is two sentences with no filler. The first sentence front-loads the action and content; the second provides usage guidance and examples. Every word contributes value.
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?
Despite having no output schema, the description adequately explains what the tool returns (description, audience, capabilities, status, contact/CTA). It also provides usage flow and product examples, making it complete for an AI agent to select and invoke correctly.
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 single parameter slug is fully described in the schema with an example. The description adds extra semantic value by listing Tuki product names (e.g., WhatsApp Booking OS, boutique ticketing) and referencing list_solutions as a source, giving the agent richer context for selecting the correct slug.
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 states the tool's function: 'Get full detail for a Tuki solution' and lists the specific content returned (description, target audience, capabilities, status, contact/CTA). It also differentiates from sibling detail tools by explicitly naming the resource type and providing examples of Tuki products.
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 gives explicit usage context: 'Use after list_solutions or when the user asks about a specific Tuki product.' This tells the agent when to invoke this tool versus alternatives and even lists example product names, providing clear guidance on when it is appropriate.
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.
Every tool targets a distinct resource or action: get_* tools retrieve details for specific entity types, search_* tools query specific verticals, and list_* tools enumerate categories. The potential overlap between search_accommodations and search_hotels is explicitly disambiguated in descriptions, and search_unified vs per-vertical searches have clear routing rules.
All tools follow a consistent verb_noun pattern: get_*_detail for lookups, search_* for queries, list_* for enumerations, and recommend as the sole one-word but still predictable exception. Naming is uniform with snake_case throughout, making the overall structure highly predictable.
With 23 tools, the server is on the heavier side, but the breadth of verticals (accommodations, cars, events, experiences, hotels, destinations, bus routes, travel guides, solutions) justifies each tool's presence. The number is slightly above the ideal but still well-scoped for a multi-category marketplace.
The tool surface covers the full browsing and recommendation lifecycle: search and list across verticals, retrieve detailed information for any entity, access editorial travel guides, and generate checkout URLs for booking. No critical operations are missing for the server's purpose as a read-only recommendation and conversion layer.