SuggestAPI exposes commerce tools through MCP and WebMCP on its Agent Gateway.
Compare two or more tenant catalog objects across price, availability, and attributes.
| Name | Required | Description | Default |
|---|---|---|---|
| ids | Yes | Catalog object ids to compare | |
| tenant | Yes | Merchant domain, e.g. demo.suggestapi.com |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses what is compared but does not state whether the operation is read-only, what the return format looks like, or how edge cases like missing attributes or fewer than two ids are handled.
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?
A single front-loaded sentence with no filler words. Every element earns its place: the verb, the resource, the minimum cardinality, and the comparison dimensions.
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 two-parameter tool, this is mostly adequate, but there is no output schema and no annotations. The agent is left without explicit information about the response shape or behavior in edge cases, such as invalid ids or objects missing certain attributes.
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 coverage is 100%, so the baseline is 3. The description adds meaningful semantics by specifying that ids must contain 'two or more' objects and by clarifying the comparison dimensions beyond the schema's generic 'Catalog object ids to compare'.
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 states a specific verb ('Compare'), a clear resource ('tenant catalog objects'), and the exact comparison dimensions ('price, availability, and attributes'). The 'two or more' qualifier distinguishes this from single-object tools like product_lookup.
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 implies use when comparing multiple catalog objects across the listed dimensions. However, it does not explicitly name alternative tools or state when not to use this tool, so sibling differentiation is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve a single canonical tenant product record by id.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Product id | |
| tenant | Yes | Merchant domain, e.g. demo.suggestapi.com |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It only states that the tool retrieves a single record, but doesn't address what happens when the id doesn't exist, whether the operation is read-only, or what the response looks like.
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?
A single sentence with zero wasted words. The verb and resource are front-loaded, and the qualifiers 'single', 'canonical', and 'by id' all earn their place by narrowing the tool's scope.
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?
Adequate for a simple two-parameter lookup tool whose schema fully documents the parameters. However, with no output schema and no annotations, key operational context like not-found behavior and read-only status 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?
Schema coverage is 100% with both parameters already documented in the input schema. The description adds marginal context ('canonical', 'by id') but doesn't meaningfully expand on parameter meaning beyond what the schema provides.
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?
Uses a specific verb ('Retrieve') and names the resource ('single canonical tenant product record'), clearly stating what the tool does. The 'by id' qualifier helps distinguish it from query-based sibling tools like search, though it doesn't explicitly name any sibling.
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 on when to use this tool versus alternatives. The description doesn't mention prerequisites, exclusions, or the conditions under which search, compare, or recommend would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a recommendation set for a tenant based on a natural-language query.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural-language recommendation request | |
| tenant | Yes | Merchant domain, e.g. demo.suggestapi.com |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden but only states that it generates a recommendation set. It does not disclose whether the operation is read-only, whether it has side effects, any authentication requirements, or what the output structure looks like.
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?
A single clear, front-loaded sentence that states the action and scope with no unnecessary detail. Every word earns its place.
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 two-parameter tool with full schema coverage, the description is minimally viable. However, it omits usage context and behavioral details such as the expected output shape or when to prefer a sibling tool, leaving the agent to infer some necessary context.
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% and both parameters already have meaningful descriptions. The tool text adds minimal extra meaning beyond framing query as a natural-language request and tenant as the scoping domain.
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 identifies the tool's function: generating a recommendation set from a natural-language query for a tenant. It is distinct in resource and intent from sibling tools like search and compare, though it does not explicitly name them.
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 sibling tools such as search, compare, or product_lookup. There are no conditions, exclusions, or alternative references in the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search a tenant's catalog and knowledge surface for products, collections, content, and policies.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | Natural-language or keyword search query | |
| tenant | Yes | Merchant domain, e.g. demo.suggestapi.com | |
| retrieval_mode | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only states what is searched. It does not mention whether the operation is read-only, how retrieval_mode changes results, pagination/limit behavior, or what the response contains, leaving important behavioral traits undisclosed.
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 a single, front-loaded sentence with no filler or redundant phrasing. It is concise and easy to parse, though its brevity comes at the cost of omitting behavioral and parameter details.
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?
Given four parameters, an enum that changes behavior, no output schema, and no annotations, this description is too thin for confident invocation. An agent would not know how retrieval_mode affects behavior, what limit controls, or what results are returned.
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 descriptions cover query and tenant, but limit and retrieval_mode have no schema descriptions and the tool description adds no meaning for them. Because schema coverage is only 50%, the description needed to compensate, especially for the retrieval_mode enum, but it does not.
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 names a specific verb ('Search') and a bounded resource ('a tenant's catalog and knowledge surface'), and enumerates the content types covered. It clearly conveys the operation, though it does not explicitly differentiate from sibling tools like product_lookup or compare.
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 scope of the description implies this is for cross-type lookup across a tenant's catalog and knowledge surface. However, there is no explicit guidance on when to prefer this over siblings like product_lookup or compare, leaving some selection to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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Each tool has a distinct role: search is broad discovery, recommend generates recommendations, product_lookup retrieves a single record, and compare evaluates multiple objects. The only meaningful overlap is between search and recommend, but their descriptions make the different return purposes clear.
Three tools use clear single-word verb names (compare, recommend, search), while product_lookup combines a noun and verb. This is a minor structural inconsistency rather than a chaotic mix, and all names are instantly understandable.
Four tools form a compact, well-scoped set for a suggestion and discovery API. Each tool covers a distinct user intent without redundancy or unnecessary surface area.
The tool set covers the core query workflows for the domain: locate items, retrieve a canonical record, get recommendations, and compare options. Management features are absent but appear outside the read-only SuggestAPI scope, so there are no meaningful gaps for an agent.