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get_service_descriptor

Get the L402/x402 service descriptor for endpoints, billing, schemas, error codes, and test endpoints. Use it to learn API call and payment requirements, pricing, and failure semantics.

Instructions

Fetch the machine-readable service descriptor for an L402/x402 service. Returns the full descriptor including endpoints, billing details, request/response schemas, stale times, error codes, refund policies, and test endpoints. Use this when you need structured metadata about how to call and pay for an API — billing units, pricing, failure semantics, and preflight verification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugNoService slug from the directory (e.g., 'satoshi-dispatches'). Used to look up the service URL if service_url is not provided.
service_urlNoBase URL of the service (e.g., 'https://dispatches.mystere.me'). The descriptor will be fetched from {service_url}/.well-known/l402-descriptor.json

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses that the tool fetches a remote descriptor and returns comprehensive details such as endpoints, billing, schemas, errors, refunds, and test endpoints. It does not discuss failure modes or network dependencies, but the core behavior is clearly conveyed.

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?

The description is compact, front-loaded with the main action and resource, and each sentence earns its place. It avoids boilerplate and immediately tells the agent what the tool does, what it returns, and when to use it.

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?

The description is largely complete for a fetch-style tool: it states the resource, return contents, and intended use case. The main gap is that it does not explicitly say one of slug or service_url must be provided, though the schema descriptions strongly imply that; this is a minor invocation ambiguity rather than a fundamental shortfall.

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

Parameters3/5

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 tool description itself adds little about slug or service_url beyond what the schema already says, though the schema descriptions are rich and cover both parameters and their relationship.

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 opens with a specific verb and resource: 'Fetch the machine-readable service descriptor for an L402/x402 service.' It enumerates the descriptor contents, making it clearly distinct from sibling tools that list, search, or get high-level service information.

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

Usage Guidelines4/5

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

The description provides a clear 'Use this when...' statement: structured metadata about how to call and pay for an API. It does not explicitly mention when not to use it or name an alternative tool, but the use case is concrete enough for an agent to route correctly.

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