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Winning AI monk playbooks

winning_playbooks
Read-onlyIdempotent

Source-linked playbooks published by successful AI monk creators (e.g. Yang Mun by Shalev): script formula, winning hooks with reported views, voice, posting cadence, funnel to product and monetization. Creator claims are unaudited.

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: results are 'source-linked' (provenance is attached) and 'creator claims are unaudited' (data reliability caveat), which an agent needs before relaying these numbers. It does not address result size or pagination behavior.

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?

A single dense sentence that front-loads the resource and its provenance, then lists the payload fields, closing with the reliability caveat. Every clause carries information; no restatement of the title or name.

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?

An output schema exists, so return structure need not be explained, and the description covers content scope plus the unaudited-claims caveat. The one remaining gap is whether this returns the full catalog at once or a bounded/paginated subset, which matters for a no-param collection tool.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so per the rubric the baseline is 4 — there is nothing for the description to disambiguate. The description correctly implies a no-argument bulk retrieval rather than a filtered query.

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 states a specific resource — source-linked playbooks from successful AI monk creators — and enumerates the contents (script formula, hooks with views, voice, cadence, funnel, monetization), which lets an agent distinguish it from get_ai_monk or search_ai_monks. It never explicitly contrasts itself with those siblings, so the differentiation is implicit rather than stated.

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

Usage Guidelines2/5

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

There is no when-to-use guidance and no alternatives named, despite four siblings (search_ai_monks, get_ai_monk, top_formats, how_they_monetize) that plausibly overlap. The agent must infer from the noun 'playbooks' alone that this is the curated-collection entry point rather than a search or lookup tool.

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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