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request_dataset_payment

Step 1 of buying a training dataset: submit the job and get back an x402 payment challenge with the exact price. Nothing is charged and nothing is generated yet. Pass the SAME arguments to create_dataset_with_payment: the server binds the payment to this exact job and refuses a mismatch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoName for the dataset
modelIdNoExact managed model from list_dataset_pricing (decides the tier and price)
networkNoChain you will pay on (default base)
gridModelNoModel on the decentralized encrypted grid: cheaper, slower, data never leaves the network
modelTierNoManaged model speed/quality tier. Omit when using gridModel
targetRowsYesHow many examples to generate (50-2000). Price scales with this
webhookUrlNoPublic HTTPS URL to notify when the dataset is ready; the POST is signed
descriptionYesWhat the model being trained should do, in one plain sentence
seedExamplesYes5 to 20 example conversations showing the behaviour you want
systemPromptNoStanding instruction every generated example should follow

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It clearly discloses that no charge occurs, nothing is generated, and the tool only returns a payment challenge with the exact price. It also reveals the binding behavior between this request and the later payment call. It could go further by describing the challenge's format or expiration, but the key behavioral traits are covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero filler, and the most important context is front-loaded: step 1, purpose, and the no-charge/no-generation guarantee. The binding requirement is stated efficiently without repeating schema information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 10-parameter, no-output-schema tool, the description does a good job explaining the workflow position, the payment challenge result, and the critical argument-matching requirement. It doesn't describe the structure of the x402 challenge or how to complete the payment step, but enough guidance is given for an agent to proceed correctly with the named sibling tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful cross-parameter semantics: all arguments sent here must be identical to those sent to create_dataset_with_payment, because the server binds the payment to the exact job. This is not present in the schema and clarifies the contract around the parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: submit the dataset job and receive an x402 payment challenge with the exact price. It also explicitly characterizes this as 'Step 1 of buying a training dataset' and distinguishes itself from the sibling create_dataset_with_payment by noting that nothing is charged or generated yet.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit placement in the workflow: this is step 1, and the same arguments must later be passed to create_dataset_with_payment. It also explains the consequence of mismatched arguments—the server refuses—which tells the agent exactly how to use this tool correctly relative to its sibling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.4/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness3/5

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.

Resources