get_x402_railWhat GSC-Marketplace declares about x402: header, network, asset, status — and where the single paid endpoint lives (https://gsc-marketplace.ai/api; its terms are stated in its 402 payload only, never here). Door: https://gsc-marketplace.ai/x402 · skill: x402-settlement.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden. It does state that the gsc-marketplace.ai/api endpoint's terms live only in its 402 payload and never the tool description, which is a genuine behavioral disclosure. But it never says whether calling this getter is side-effect free, whether it reaches a remote endpoint, or how the returned declaration is formatted.
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 text is short, but not especially well structured. The first sentence packs list, network, asset, status, and terms caveats into a run-on; the second adds 'Door' and 'skill' without explaining what those terms mean for an invoker. It is telegraphic rather than simple.
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 zero-param unput getter, the tool requires almost no parameter setup, yet an Agent needs to know what the response contains, and whether it is safe to call remote. The description lists the domains of information (header, network, asset, status) and the endpoint, but it does not explain the response structure or what 'capability declaration' actually returns.
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 input schema has zero properties and zero required parameters, so there is no parameter documentation gap to fill. The baseline for a caller with no parameters is high, and the description adds small value by contextualizing what the tool declares. Nothing about parameters is missing.
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 the concrete verb-resource shape: it says the tool reports 'what GSC-Marketplace declares about x402' and enumerates the scope (header, network, asset, status, endpoint). That gives the agent a real sense of what the tool retrieves. It is not a tautology, and the content separates it from mere 'get' across ancestors. However, it does little to distinguish itself from sibling tools by name alone.
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?
There is no explicit statement of when an agent should call this tool versus contact_trading_desk or any other sibling. The description indirectly implies an agent calls it to inspect the x402 declaration, but it gives no exclusions, no prerequisites, and no guidance for choosing it over alternatives.
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 has a clearly distinct outcome: search, verify, identifier policy, graph overview, market operators, standards, payment-rail context, and human/agent escalation. Even the closely related search_brands → get_brand → get_brand_identifiers path is explicitly staged as a workflow, and contact_trading_desk vs instant_message_agents is delimited by audience.
Tool names follow a predictable lowercase snake_case pattern: mostly get_<resource>, with action verbs search_, contact_, and instant_message_ for the non-read operations. The naming style is consistent throughout, and the less conventional instant_message_agents is still recognizable as verb-plus-object.
Eleven tools fits the ideal 3–15 range for a coherent agent-facing service. Each tool covers an identifiable slice of the domain—discovery, verification, identifiers, market context, standards, messaging, or settlement context—and none feel redundant.
The core sourcing workflow is covered: FOUND via search, VERIFIED via get_brand, ANSWERABLE via get_brand_identifiers, then human/agent follow-up. The main gaps are the referenced resolve_gtin/resolve_sparks/check_eligibility tools and any purchase/pricing/order-writing surface, but those appear intentionally externalized through doors and transaction MCP.