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unlock_ask

Separate $1 reader checkout for an ask you did not post. Same as POST /api/asks/:id/checkout. Returns a flat object with top-level url, mode, and unlock_token. Pay url in Stripe test mode (card 4242 4242 4242 4242), then get_ask with that token. The token reads answers only after payment. HTTP 409 while awaiting_answers is true, so a reader does not pay for an empty thread. Does not POST to the asker's callback_url. Every Checkout session is USD and adaptive pricing is off. Poll unlock $1. Asker posts $1–$5. Reader unlock $1. Answers are free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ask_idYes
payer_typeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
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: it discloses the return shape (url, mode, unlock_token), the test-mode card to pay with, that the token reads answers only after payment, the 409 awaiting_answers guard, that it does NOT POST to the asker's callback_url, and that pricing is USD with adaptive pricing off. These are exactly the side effects and payment semantics an agent needs.

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

Conciseness3/5

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

The purpose is correctly front-loaded in the first sentence, but the tail becomes fragmented and partly off-topic ('Poll unlock $1. Asker posts $1–$5. Reader unlock $1. Answers are free.') — pricing for the asker/answers does not help invoke this tool. The run-on sentences dilute an otherwise information-dense description.

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?

For a payment-flow tool with no annotations and no output schema, the description covers return values, error behavior, side effects, and payment mode thoroughly. The remaining gap is parameter documentation (especially payer_type), which is the one thing an agent still cannot resolve.

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

Parameters2/5

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

Schema coverage is 0% and neither parameter is explained. The description implies ask_id's role ('an ask you did not post') but never documents the payer_type enum (human/agent), leaving a required decision undocumented in both schema and description. It does not compensate for the coverage gap.

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 verb+resource ('Separate $1 reader checkout for an ask you did not post') and pins it to an endpoint, making clear it is the reader-side unlock rather than create_checkout or unlock_poll. An agent can distinguish it from all siblings without opening a schema.

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?

Gives clear context for use ('for an ask you did not post'), a follow-up step ('then get_ask with that token'), and a precondition (409 while awaiting_answers is true, so you don't pay for an empty thread). It stops short of explicitly contrasting with create_checkout or unlock_poll, so it is strong context rather than full when/when-not routing.

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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