Get x402nano pricing
get_market_pricingReturns public MCP pricing, network, asset, seller, and payment metadata without making a payment.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_market_pricingReturns public MCP pricing, network, asset, seller, and payment metadata without making a payment.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the operation is public and involves no payment, which are key behavioral traits for a read-only tool. It does not detail response format or data freshness, but given the zero-parameter scope, this level of disclosure is adequate.
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, front-loaded sentence with no wasted words. Every word contributes: 'Returns' sets the action, the list of metadata types specifies scope, and 'without making a payment' clarifies behavior.
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?
Given the tool's simplicity (no parameters, no output schema, no annotations), the description is quite complete. It covers the primary purpose and the key behavioral aspect (no payment). It could mention the exact return structure, but the listed categories provide sufficient context for an agent to invoke the tool correctly.
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 has zero parameters, so the baseline is 4. The description adds value by enumerating the categories of data returned (pricing, network, asset, seller, payment metadata), which effectively explains what the tool does with no input.
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 the verb 'Returns' and specifies the resource: 'public MCP pricing, network, asset, seller, and payment metadata'. This clearly differentiates it from sibling tools (get_market_brief, get_market_delta, list_trending_markets) by focusing on pricing and metadata rather than briefs, deltas, or trends. The title adds specificity with 'x402nano'.
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 phrase 'without making a payment' provides clear context that this is a free, non-transactional lookup, which implies it should be used when pricing/metadata is needed without a payment. It does not explicitly mention alternatives or exclusions, but the context is sufficiently clear for an agent to select this tool over siblings.
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 targets a distinct action: listing trending markets, purchasing a brief, purchasing a delta report, and retrieving service pricing metadata. There is no functional overlap, and the descriptions clearly differentiate the outputs.
Most tools use 'get_' for acquiring resources, while 'list_trending_markets' uses 'list_' for enumeration. This is a common and predictable convention, so the naming is mostly consistent with only a minor deviation.
With only 4 tools, the server is tightly scoped to its purpose of providing market intelligence, avoiding unnecessary bloat while covering the essential functions.
The tool set covers the full workflow: discover trending markets, understand pricing, and purchase two types of market analyses. No obvious gaps exist for the stated domain of prediction-market intelligence.