How a purchased dataset is coming along, and the download link once it is ready. Returns rows done vs target while generating, a presigned JSONL download URL (valid 5 minutes, re-issued each call) when complete, or the refund state if generation failed.
| Name | Required | Description | Default |
|---|---|---|---|
| datasetId | Yes | dataset_id from create_dataset_with_payment | |
| claimToken | Yes | claim_token from create_dataset_with_payment |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It usefully reveals that the download URL is valid for 5 minutes, is re-issued on each call, and that failure returns refund state. Minor details like rate limits or error shapes are not covered, but the core behavior is transparent.
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 dense sentence with no filler. It front-loads the overall purpose and then enumerates the three possible return states, with the URL validity detail earning 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?
For a simple two-parameter tool with no output schema, the description covers all lifecycle states: generating, complete, and failed. It also explains the download URL behavior, making the tool fully understandable without needing additional context.
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?
Schema description coverage is 100%, and both parameters are already explained as values coming from create_dataset_with_payment. The description adds no extra parameter-level meaning, 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 clearly states the tool reports the status of a purchased dataset and provides the download link when ready. It names specific return artifacts (rows done vs target, presigned JSONL URL, refund state), which distinguishes it from creation and payment sibling 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 the intended context: poll after purchase while the dataset is generating, then retrieve the URL once complete or the refund state if it failed. It does not explicitly name alternatives or exclusions, so it stops just short of full guidance.
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 pair a clear verb with a distinct resource noun, so endpoints, agents, datasets, campaigns, products, and vault operations are generally easy to separate. The main risk is the repeated request_*_payment / *_with_payment pairs and the two create_*_with_payment tools, which are only distinguishable by reading descriptions carefully.
The dominant pattern is verb_noun snake_case, such as list_my_endpoints, update_campaign, and finalize_agent_registration, and the payment/request pairs are consistently named. The vault_* tools use a namespace prefix rather than verb-first names, and vault_how_to_backup breaks the action-oriented convention, so it is not perfectly uniform.
48 tools is a very large MCP surface, well beyond the 25-tool threshold where selection becomes a serious burden. Even though the server spans multiple domains, the count feels excessive for a single agent-facing tool set and should likely be split by domain.
The core endpoint, agent, marketplace, and payment flows are well covered, including creation, updates, stats, and two-phase x402 purchases. However, there is no create_product or delete_product, campaigns lack delete, and there is no way to list previously purchased datasets, leaving notable lifecycle gaps.