get_productGet a single product by slug
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug from the catalog |
| Name | Required | Description | Default |
|---|---|---|---|
| ok | Yes | True when the upstream storefront call returned a 2xx response | |
| httpStatus | Yes | Upstream HTTP status code | |
| paymentRequired | No | True when the response is an x402 HTTP 402 payment challenge |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description need not repeat these. However, the description adds no additional behavioral context such as error handling, authorization, or not-found behavior, providing no value beyond the 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 a single, focused sentence with no redundant wording. It is appropriately concise for a simple retrieval operation and front-loads the key action.
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 the tool's simplicity (one parameter, read-only, idempotent, output schema present), the description is complete enough. It fully conveys the purpose and mechanism without needing to elaborate on return values or edge cases, as the schema and annotations cover the rest.
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 provides 100% coverage for the slug parameter with its description 'Product slug from the catalog'. The description's mention of 'by slug' merely restates the parameter and does not add semantic meaning beyond the schema.
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 'Get a single product by slug' clearly specifies the action (get), resource (product), and identifier (slug), effectively distinguishing it from sibling tools like list_products. It is concise and 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 implies the tool is for retrieving one product when its slug is known, but it does not explicitly state when to use it over alternatives or exclude other cases. No when-to-use or alternative guidance is provided beyond the basic 'by slug' context.
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 clear, distinct purpose. Financial tools (bond, cashflow, deal, options, portfolio, wallet, waterfall) each target a specific analysis type, email tools are batch vs single, and e-commerce tools are separate. No two tools could be easily confused.
Most tools follow a noun_verb pattern (e.g., bond_analyze, emailguard_validate), but there is some inconsistency: some use verb_noun (get_product, list_products) and brand names like cashflowlens_analyze break the pattern slightly. Overall, it is still readable and mostly predictable.
With 19 tools spanning finance, email, security, and e-commerce, the count is slightly high but reasonable for a pay-per-use server offering diverse deterministic analytics. Each tool serves a distinct function, and the number is not overwhelming.
The set covers major financial analysis types, email validation, and basic e-commerce operations. Minor gaps (e.g., no tool for portfolio rebalancing or more advanced email features) exist, but the core advertised services are well-covered.