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 the tool is not read-only, not idempotent, and not destructive. The description adds meaningful behavioral context: state-changing nature, Stripe side effect, one-time claim URL, and the human-approval precondition. Nothing contradicts 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?
Two sentences with no filler: the precondition is front-loaded, followed by the action and value-parameter guidance. Every clause contributes a distinct piece of 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 one-parameter mutation tool with no output schema, the description covers purpose, precondition, and option semantics well. It does not explicitly describe what the tool returns or behavior on repeated calls, leaving a small but real gap.
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?
With 0% schema description coverage, the description carries the full burden for the single 'plan' parameter. It maps each enum value to its price and use case: '$5 pass for research or $19/month subscription for recurring workflows.' This is exactly the semantic information an agent needs to choose correctly.
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 action — creates a Stripe Checkout plus one-time claim URL — and ties it to a payment-access context. It is clearly distinguished from sibling tools like request_capability by involving paid Stripe checkout and explicit pricing.
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?
It explicitly says to use the tool only after free samples demonstrate value and the human owner approves payment, which is strong when-to-use guidance. It does not name a non-paid alternative tool for the same capability, so it falls just short of full when-not/alternatives coverage.
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 maps to a distinct lifecycle stage: discovery, materialization, sampling, querying, aggregation, and payment. The main ambiguity is between search_public_datasets and search_discovered_datasets, plus some overlap between get_coverage_status and list_official_sources, but the descriptions provide enough guidance for most selections.
All tools use a consistent verb_object snake_case pattern with clear verbs: get_, list_, query_, request_, sample_, search_, and aggregate_. State-changing actions uniformly use request_, and status reads uniformly use get_.
With 14 tools, the server is well within the ideal range and each tool earns its place across the data lifecycle: discover, materialize, sample, query, aggregate, and manage access. The count feels complete without being padded.
The set covers discovery, materialization, sampling, querying, aggregation, coverage status, and paid access, with provenance embedded throughout. Minor gaps exist around the 'insights' promised in the server name and lifecycle operations like cancellation or removal, but agents can generally complete core workflows.