Get Aivonic's current pricing: the Aivonic Workspace SaaS plans (monthly), and the done-for-you AI Agent, Multi-Agent, and Voice AI packages (one-time setup fee + monthly support). All figures in USD.
| Name | Required | Description | Default |
|---|---|---|---|
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the nature of the data (current pricing, in USD, monthly vs one-time) and adequately conveys that this is a read-only informational tool. However, it does not mention potential staleness or whether the data is dynamic.
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 resource. Every clause adds meaningful detail (pricing types, payment structure, currency) without redundancy.
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?
The tool is simple (no params, no output schema), and the description fully covers what it returns and the units. It is complete for its purpose, leaving no critical gaps.
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 tool has zero parameters, so the baseline is 4. The description enriches the empty schema by explaining what the tool returns, adding value beyond the structured data.
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 'Get' with a clear resource ('Aivonic's current pricing') and enumerates the exact categories (SaaS plans, done-for-you packages). This distinguishes it from sibling tools like get_products or book_demo, making its purpose unambiguous.
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 defines what this tool returns, which implies it is for pricing inquiries. It implicitly differentiates from siblings by listing pricing-specific details, but it does not explicitly state when to use this versus alternatives or provide exclusions.
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 has a distinct purpose: checking availability, booking, fetching pricing, fetching product info, and requesting quotes. Even the two booking-related tools (check_availability and book_demo) have clearly separated roles.
All tools follow a consistent verb_noun pattern (book_demo, check_availability, get_pricing, get_products, request_quote). This makes the tool set predictable and easy to navigate.
Five tools is well-scoped for a server focused on discovery call booking and product/pricing information. Each tool serves a distinct need without redundancy or bloat.
The core workflow is covered: check availability, book, and get info. A minor gap is no explicit cancel or reschedule endpoint, but that is not essential for the primary use case of booking a demo and obtaining information.
