dp_commercial_get_sample_reportReturn an illustrative synthetic report sample without customer data. This is not a service trial and must never be represented as measured customer performance.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| ok | Yes | ||
| data | Yes | ||
| error | Yes | ||
| links | Yes | ||
| version | Yes | ||
| evidence | Yes | ||
| nextAction | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds valuable context beyond annotations: the result is synthetic, illustrative, contains no customer data, and must not be misrepresented as real measured performance.
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 two sentences with no filler. The core purpose is front-loaded, and the second sentence adds an essential guardrail. Every sentence 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?
With zero parameters, an output schema, and annotations covering read-only/idempotent behavior, the description provides the key missing semantic context: the output is synthetic and must not be mistaken for real customer performance. Nothing material 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 and the empty schema is fully covered. No parameter explanation is needed, and the baseline of 4 for no-parameter tools 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 states a specific verb ('Return') and resource ('illustrative synthetic report sample'), and explicitly clarifies it contains no customer data. It also distinguishes the tool from a service trial by stating it is not one, which helps differentiate it from sibling trial/status tools.
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 this tool should be used to retrieve a synthetic sample report and explicitly warns against representing it as measured customer performance. However, it does not state when to prefer this over sibling tools such as dp_commercial_get_live_trial_status or dp_commercial_get_offer.
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 a clear, distinct target, and overlapping trial/checkout/status tools are cross-referenced in their descriptions. A few pairs such as get_checkout_readiness versus get_checkout_status and legacy claim_credentials versus start_sandbox could still be misselected, but the descriptions generally resolve the boundaries.
All 28 tools share the dp_commercial_ prefix and follow a consistent snake_case verb_noun pattern such as get_, list_, start_, compare_, and request_. There is no mixing of camelCase, inconsistent verbs, or unpredictable naming.
At 28 tools, the server is above the 25-tool threshold and feels heavy even for a broad commercial platform. Several informational tools, such as checkout readiness versus checkout status and service status versus uptime history, could be consolidated without losing core capability.
The toolset covers the core commercial lifecycle well: discover needs, compare plans, assess fit, start trial/checkout, request proposals, check status, and review security/compliance profiles. Minor gaps exist around post-checkout subscription management or cancellation, but the described handoff to the DigitalPublic product covers most dead ends.