list_merchantsList public AirShelf merchants that have an assigned slug and at least one active product. Internal/demo and empty catalogs are excluded. Returns each merchant's name, slug, and product count; the slug is the merchant argument for request_quote.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| total | No | ||
| merchants | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds meaningful behavioral details: it returns each merchant's name, slug, and product count, and explicitly links the slug to the `merchant` argument of request_quote. It also clarifies exclusion criteria, which goes beyond what annotations convey.
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 concise sentences cover the full scope: the main action, filtering criteria, exclusions, and return values, plus a practical cross-reference to request_quote. Every word earns its place, with no 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?
For a parameterless list endpoint with an output schema and read-only annotations, the description fully covers purpose, filtering, exclusions, return contents, and downstream usage of the slug. Nothing an agent needs to understand or invoke this tool is missing.
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 schema provides no parameter information. The description does not attempt to document parameters because none exist. Following the baseline for 0 parameters (4), this score reflects that no further parameter semantics are needed or expected.
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 a specific verb ('List') and clearly identifies the resource (public AirShelf merchants) and the exact filters (assigned slug, at least one active product). It does not explicitly contrast with sibling tools, but the enumeration of exclusions (internal/demo, empty catalogs) adds precision beyond a generic description.
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 implies when to use the tool by specifying that only public merchants with active products are returned, and it notes exclusions (internal/demo, empty catalogs). However, it does not name any alternative tools or provide an explicit 'use X instead' guidance, leaving the comparison to siblings implicit.
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.
The set contains at least two genuinely confusable pairs: find_products vs research_shopping (explicitly cross-referencing each other in their own descriptions, with near-synonymous purposes of discovering products), and get_product vs explain_product vs compare_products, all of which return product records with prices and specs. The descriptions go to great lengths to disambiguate, which flags the very real boundary problem — an agent would struggle to pick correctly without reading thousands of characters.
All eight names follow a clear verb_noun snake_case pattern (compare_products, get_product, search_catalog, request_quote, research_shopping). Minor deviations: singular/plural inconsistency across pairs (get_product vs compare_products), and research_shopping reads as a noun phrase rather than verb+object. Otherwise, predictable and clean.
Eight tools is right in the sweet spot for a research/quoting workflow, and each earns its place: search, find, get, compare, explain, merchants, quote, and the heavy research job. The surface is neither bloated with near-duplicate operations nor so thin that the server feels like a stub.
The buyer-to-quote lifecycle is covered end to end: discovery (search_catalog, find_products), deep verification (get_product, explain_product, tier3 live lookup), comparison, merchant lookup, and a double opt-in request_quote flow that closes the loop. Minor gaps: no way to list prior quotes placed or track a quote's status after the initial response, and no explicit search-catalog pagination control, but agents can work around these.