Generate a wholesale pricing quote for a list of POPS4 products with quantities. Returns line items, unit prices, line totals, grand total, and contact information for Jenny Huang Goodman to finalize the order.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | Array of products and quantities to quote |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses what the tool returns (line items, totals, contact info) and implies the quote is not a final order by mentioning 'to finalize the order'. However, it does not explicitly state whether the operation is read-only, side-effect free, or requires authentication, which is a gap for a tool with no 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 waste. The first sentence states the purpose, the second summarizes return values and the next step. All content is relevant and front-loaded, making it easy for an agent to parse quickly.
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 tool with one parameter and no output schema, the description covers the primary function and return values, which is adequate. However, the presence of 'get_live_quote' sibling creates ambiguity about when to use this tool, and the description does not address this, leaving a completeness gap.
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 description coverage is 100%, so the baseline is 3. The description adds minimal meaning beyond the schema; it repeats 'list of POPS4 products with quantities' without providing additional format, constraints, or examples. It does not compensate beyond what the schema already documents.
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 verb 'Generate' and the resource 'wholesale pricing quote for a list of POPS4 products with quantities'. This is specific enough to understand the core function. However, it does not explicitly differentiate from the sibling tool 'get_live_quote', which appears to be a similar alternative.
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?
No guidance is provided on when to use this tool versus alternatives like 'get_live_quote'. The description implies it is for wholesale pricing but does not state exclusions or prerequisites, leaving the agent without direction on tool selection among siblings.
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.
get_quote and get_live_quote both return wholesale quotes for product SKUs and quantities, differing only in context (enterprise events vs. general). get_product_catalog and search_products both search the catalog with filters, making the boundary between them unclear. These overlaps can lead to agent misselection.
All tools use snake_case with a verb_noun pattern (get_, generate_, search_, track_). There are minor deviations where similar actions use different verbs (e.g., get_product_catalog vs. search_products, get_quote vs. get_live_quote), but the overall pattern is consistent and predictable.
With 10 tools, the server is well-scoped for its purpose. Each tool covers a distinct aspect of the procurement and event management domain, and the count falls within the ideal 3-15 range, earning its place without feeling bloated.
The server covers product discovery, catalog browsing, quotes, event program generation, and shipment tracking. However, there is no tool for placing an order or managing event records, and the link between quotes and orders is unclear, leaving notable gaps in the end-to-end procurement workflow.