request_paid_accessUse this state-changing tool only after free samples demonstrate value and the human owner approves payment. Creates a Stripe Checkout plus one-time claim URL; choose the $5 pass for research or $19/month subscription for recurring workflows.
| Name | Required | Description | Default |
|---|---|---|---|
| plan | No | pass |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false and idempotentHint=false, and the description reinforces state-changing behavior while adding concrete details: it creates a Stripe Checkout and a one-time claim URL. This goes beyond the bare annotation profile, though it could further clarify that this results in a real payment charge or is not reversible.
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?
Two sentences with no filler. The critical usage gate is front-loaded, followed by the concrete outcome and plan-selection guidance. Every clause contributes useful information.
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 single-parameter tool with no output schema, the description covers purpose, preconditions, created artifacts, and plan selection. It does not describe the exact response shape or how the one-time claim URL is returned, but that is a minor gap for correctly invoking the tool.
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 0% description coverage, so the description carries all semantic weight for the plan parameter. It explains both enum values with price and intent: '$5 pass for research or $19/month subscription for recurring workflows.' This fully compensates for the missing schema descriptions.
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 concrete verb and resource: 'Creates a Stripe Checkout plus one-time claim URL.' It clearly differentiates this from sibling request tools like request_capability and request_dataset_materialization by emphasizing a paid access flow and specific plan options.
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 gives explicit usage conditions: 'only after free samples demonstrate value and the human owner approves payment.' It also tells the agent when to choose each plan ('$5 pass for research' vs '$19/month subscription for recurring workflows'). It does not explicitly name sibling alternatives, but its context is strong enough to guide calling correctly.
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 map to distinct lifecycle stages—discovery, materialization, sampling, querying, and access—but several discovery tools overlap in purpose. search_public_datasets and search_discovered_datasets both return catalog matches, and list_official_sources and get_coverage_status both describe coverage. The descriptions help separate them, but an agent could still misselect without careful reading.
All tool names follow a consistent snake_case verb_noun pattern, such as search_, list_, get_, request_, sample_, and query_. The noun phrases are descriptive and parallel, making the naming predictable across the entire set.
Fourteen tools is well within the ideal range for a public-data platform and covers discovery, materialization, sampling, querying, aggregation, coverage monitoring, capability requests, and paid access. Each tool has a justified role in the workflow, with no obvious bloat.
The tool surface covers the full data lifecycle: discover sources, search datasets, request materialization, poll status, sample, query, aggregate, and request missing capabilities. It also includes billing access and Federal Register-specific workflows, leaving no obvious dead ends for the stated domain.