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brianbooms

Quiet Menders MCP Server

qm_sleep_queue

Generate an ordered track list and runtimes that fill a requested number of hours from a sleep catalog for sleep, focus, meditation, calm, or deep sessions; pay per call via x402.

Instructions

Ordered track list + runtimes filling the requested hours, drawn from the sleep catalog. Price: $0.10 USDC on Base via x402 (the live 402 challenge is authoritative). Two-phase: call without 'payment' to get the live payment requirements (payTo, amount, accepted networks); pay from your own wallet, then re-call with 'payment' set to the base64-encoded x402 payload — the data comes back in the tool result. This tool never pays, never touches private keys, and never holds funds. Resale permitted: you may resell this data output at your own price.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
moodNosleep | focus | meditate | calm | deep (default sleep).
hoursNo0.25-12, default 1.
paymentNoBase64-encoded x402 v1 payment payload (the signed ExactEvmPayload JSON). Omit on the first call to receive the payment requirements; sign in your own wallet / x402 client, then re-call with this set. The server only forwards it to the rail — it never signs, never holds funds, never touches private keys.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so richly: discloses price/rail ($0.10 USDC on Base via x402), the authoritative 402 challenge, that it never pays, touches keys, or holds funds, and that resale is permitted. This is exactly the behavioral context an agent needs before invoking.

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?

Front-loaded with the core purpose, then payment mechanics and safety posture. Dense and largely waste-free, though the resale/pricing sentences, while useful, add length that could be trimmed.

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

Completeness5/5

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

For a paid, two-phase tool with no output schema, the description covers the return path ('the data comes back in the tool result'), payment acquisition, and safety guarantees. An agent has everything needed to call it correctly.

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 all three parameters are already documented in the schema, including the two-phase 'payment' semantics. The description reinforces but adds little beyond what the schema states, so baseline 3 applies.

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?

States a specific deliverable (ordered track list + runtimes filling requested hours) and its source (sleep catalog). This clearly distinguishes it from siblings like qm_sleep_recommend or qm_playlist_build.

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?

Explicitly lays out the two-phase call workflow: first call without 'payment' to get requirements, then re-call with the payload. However, it never contrasts this tool with the sleep-adjacent siblings (qm_sleep_recommend, qm_data_sleep_tip), so an agent must infer when this is the right choice over those.

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