Return the 70,000+ product catalog for AI-native corporate gifting. Filter by keyword, brand, or category. With no filter, returns the category tree with counts.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | No | Brand filter | |
| limit | No | Max results (default 20, max 50) | |
| filter | No | Keyword filter on product title | |
| category | No | Category filter |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the behavioral disclosure burden. It adds a useful non-obvious behavior (no filter returns category tree with counts) but omits details like pagination, response format for filtered queries, or any access/rate-limit considerations. It is not misleading but lacks depth.
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-loaded with the primary purpose and total catalog size. Every sentence contributes: the first defines the tool, the second explains filter and default behavior. No redundancy or fluff.
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 description plus schema is largely complete for invocation: all parameters are documented, and the description clarifies the unfiltered response behavior. However, with no output schema, it does not describe the return shape for filtered queries, which would be helpful; still, the core functionality is clear.
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 has 100% coverage, describing each parameter (brand, limit, filter, category). The description echoes filter, brand, and category but adds no additional semantic meaning, especially for the limit parameter. Baseline of 3 is appropriate when schema alone fully documents parameters.
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 returns 'the 70,000+ product catalog' with a specific verb and resource, and details filtering options. It distinguishes itself from siblings like get_product and search_products by noting the full catalog scope and the no-filter category tree behavior.
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 provides clear context by explaining behavior with and without filters ('With no filter, returns the category tree with counts'), which implies when to use this tool. However, it does not explicitly mention alternatives or exclusions relative to sibling tools like search_products.
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.