start_checkoutStart a Stripe checkout session for a product slug
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Product slug to purchase |
| 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 convey that this is non-read-only, non-idempotent, and has external side effects (openWorldHint true). The description adds context that it is a Stripe checkout session, implying a payment flow, but does not disclose additional behavioral details like session expiration or redirect behavior. No contradiction with 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 concise sentence that immediately states the tool's purpose. There is no redundant or unnecessary wording.
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?
This is a simple one-parameter tool with full schema coverage, an output schema, and safety-relevant annotations. The description, combined with the schema and annotations, provides sufficient context for an agent to select and invoke the tool correctly.
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 schema description for the single parameter 'slug' is fully provided ('Product slug to purchase'). The description merely restates 'for a product slug' without adding additional 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 clearly identifies the action (starting a checkout session), the resource (Stripe checkout session), and the required input (product slug). This differentiates it from sibling tools like get_checkout_policies or list_products.
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 (when starting a checkout for a product) but does not explicitly mention alternatives or exclusions, such as when to prefer purchase_with_x402 or get_checkout_policies.
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.