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?
The annotations already declare readOnlyHint=true and idempotentHint=true, so the agent knows this is a safe read operation. The description adds no behavioral context beyond the obvious get operation, such as error handling or permissions, so it doesn't provide additional transparency 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, well-formed sentence that immediately states the action, resource, and parameter. It is front-loaded and contains no filler, scoring a 5.
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 getter tool with a single parameter, an output schema, and annotations indicating safety and idempotency, this short description is sufficient. It clearly communicates what the tool does, and the output schema covers return values, so the description is complete.
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 has 100% coverage with the slug parameter described as 'Product slug from the catalog', and the description reinforces this by saying 'by slug'. The description adds no extra semantic detail beyond the schema, so the baseline of 3 applies.
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 the specific verb 'Get' with the resource 'single product' and the unique identifier 'slug', making it clear this is a point lookup. This distinguishes it from sibling tools like list_products which would handle list retrieval.
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 usage when you need one product by slug, but does not explicitly state when to use it versus alternatives like list_products or other product-related tools. There are no exclusions or alternative tool names mentioned, so it earns a 4 for clear context without exclusions.
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.
Most tools have clearly distinct purposes. Some overlap exists (e.g., dealanalyzer_analyze combines CashflowLens and WaterfallLens; action_safety_run includes DestructGuard evidence), but descriptions explicitly clarify scope so an agent can choose correctly.
Naming patterns are mixed: some verbs precede nouns (get_product, purchase_with_x402), others follow nouns (bond_analyze, portfolioguard_score), and some are multi-word (action_safety_quick_gate). This inconsistency makes tool selection less predictable.
At 25 tools, the server is at the heavy end and spans diverse subdomains (safety, payments, financial analytics, storefront). While each tool serves a purpose, the broad scope makes it feel overloaded for a single server.
Core assurance workflows (safety, payment, verification) are covered, but gaps exist: no refund/cancellation tools, no attestation lookup, and no way to retrieve 30-day action-safety status records. Financial analytics also have limited coverage (no equities).