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Apiosk MCP Server

Apiosk MCP Server

by obcraft
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

apiosk_help

Read-only

Get guidance on Apiosk MCP: understand setup, authentication, USDC/x402 payments, and recommended workflows for discovery, wallets, and publishing.

Instructions

Explain what Apiosk MCP is, how to connect it, how auth and USDC/x402 payments work, and the recommended workflow for discovery, wallets, and publishing.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "apiosk_help"
  }
}

Input Schema

NameRequiredDescriptionDefault
topicNoOptional help topic. Defaults to overview. Use 'discovery' to learn which live sources apiosk_discover searches (Apiosk catalog + Coinbase Bazaar + well-known); use 'rails' for how USDC/x402 settlement works.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.8.0

TDQS

B3.4/5.0
Behavior3/5

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

The annotations already establish that this is a non-destructive read-only call, and the description is consistent. It adds a useful outline of the returned explanation content but does not disclose deeper behavior such as whether the help is static, rate-limited, or dependent on auth state.

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

Conciseness4/5

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

The description is a single front-loaded sentence with listing but no redundancy. It efficiently enumerates what the tool explains, though the long list of topics makes it slightly dense.

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

Completeness3/5

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

Given the optional single parameter, the schema's enum documentation, and the presence of an output schema, the description is mosely sufficient for a help tool. The main gap is its relationship to closely related sibling help tools, which context does not clarify.

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 coverage for the single optional 'topic' parameter is 100%, and the enum values are self-descriptive, so the description does not need to add much. It provides a light mapping of topics (auth, payments, workflow) but relies on the schema for detailed parameter meaning.

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

Purpose4/5

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

The description clearly states the tool's job with a verb ('Explain') and a distinct resource (Apiosk MCP), listing specific areas: connection, auth, USDC/x402 payments, and workflow. It does not explicitly differentiate from overlapping siblings like apiosk_get_started or apiosk_payment_guide, so it falls short of a 5.

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

Usage Guidelines3/5

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

The description implies the tool is a general help/educational entry point, but it never states when to prefer it over sibling tools. The schema's topic enum partially compensates by directing the agent to use 'discovery' and 'rails' for specific learning goals, so there is some contextual guidance.

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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