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Get executable plan (paid)

get_plan

Use when you decided on a platform and want the executable step-by-step plan (bot/human split, human minutes, order, checklist against ban reasons, DE tax/payout notes with sources). Paid: 0.25 USD per plan; payment via x402 (USDC on Base) over HTTP: this tool returns the price, the x402 payment requirement and a free preview; pay and fetch the full plan with GET /plans/{platform} + PAYMENT-SIGNATURE header. Plans exist for: adobe-stock, etsy, spreadshirt, redbubble, gumroad, apify.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformYesOne of: adobe-stock, etsy, spreadshirt, redbubble, gumroad, apify
preview_onlyNoOnly return the free preview

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
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: exact price (0.25 USD/plan), payment rail (x402, USDC on Base), what this call returns (price, payment requirement, free preview), and how to obtain the full plan (GET /plans/{platform} with PAYMENT-SIGNATURE). Cost and payment/auth prerequisites are exactly the behavioral facts an agent needs before calling.

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?

Front-loaded with the usage trigger, then the value proposition, then payment mechanics, then valid platforms. It is dense but every clause carries information the agent needs; nothing is redundant filler.

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?

There is no output schema, yet the description compensates by stating precisely what is returned (price, x402 requirement, free preview) and how to get the full artifact. Combined with the enumerated platform set and payment path, an agent has everything required to invoke and follow up 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% with only 2 simple params, so the schema already documents both. The description reinforces the preview concept ('returns ... a free preview') and re-lists the valid platforms, but adds no syntax or constraint detail beyond the schema. 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 resource ('executable step-by-step plan') plus its contents (bot/human split, human minutes, order, ban-reason checklist, DE tax/payout notes) and the trigger ('you decided on a platform'). This is clearly separable from siblings like recommend, get_platform, or list_platforms, which operate at earlier decision stages.

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 opening 'Use when you decided on a platform' gives an explicit precondition that implicitly excludes the earlier-stage siblings (recommend/can_i_earn_here) without naming them. It also explains the paid flow and enumerates which platforms have plans. It stops short of explicitly naming the alternative tools to use beforehand.

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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