What a generated training dataset costs and which models can generate it: per-100-example rates for each managed tier plus the decentralized grid, the maximum size, and the model catalog with published per-million-token rates. No auth, no payment. Call this before quoting a job.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral burden. It discloses that the call requires no authentication and no payment, which is useful context beyond the tool name. Since it is a listing tool, this adequately communicates that there are no destructive or transactional side effects.
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 compact and front-loaded: the first clause immediately states what the tool returns and the final sentence gives a direct call-to-action. Every sentence earns its place, and there is no redundant or generic filler.
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 parameterless read-only tool with no output schema, the description covers everything an agent needs: what data is returned, the units of pricing, the absence of auth/payment requirements, and when to call it. Nothing critical 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 takes zero parameters and the schema is empty, so there is no parameter information for the description to add. The baseline of 4 applies here, and the description sensibly focuses on what the response will contain rather than on inputs.
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 uses a specific verb and resource: 'list' + 'dataset pricing', then details exactly what the agent will get: per-100-example rates, managed tiers, grid, maximum size, and model catalog rates. It clearly distinguishes itself from payment- and creation-focused siblings by stating 'No auth, no payment'.
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 guidance on when to call this tool: 'Call this before quoting a job.' It also clarifies that no auth or payment is required, signaling this is a lightweight informational lookup. It does not explicitly name alternatives, but no close pricing-specific sibling exists in the provided list.
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.