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roastify_check_payment

Check the payment status of a Lightning invoice.

Call after paying the invoice from purchase_credits. Free — no credits required. Proof of npub ownership is required to prevent credit-grant front-running by an observer of the invoice ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
npubYesThe Nostr public key (npub1...) that purchased the invoice.
dpop_tokenYesRaw JSON of a kind-27235 Nostr event signed by npub — not base64, not NIP-98 'Authorization: Nostr <b64>' framing. Its `u` tag must hold THIS tool's exact name (from tools/list), not the endpoint URL; content:"", created_at within 60s of now, and a random `nonce` tag recommended. Or a cached dpop_token phrase.
invoice_idYesThe invoice ID returned by purchase_credits.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the transparency burden. It discloses the cost model ('Free — no credits required') and an important security requirement ('Proof of npub ownership is required to prevent credit-grant front-running'). This goes beyond a generic read operation, though it does not describe all possible side effects or response 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?

The description is compact and front-loaded with the core purpose, followed by a single usage directive and two key behavioral constraints. Every sentence earns its place with no redundant phrasing.

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?

The combination of a clear purpose, explicit sequencing after purchase_credits, cost information, authentication context, and a complete input schema is sufficient for correct invocation. The presence of an output schema reduces the need to describe return values. Minor missing details like polling behavior or possible statuses are covered by the output schema.

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 the parameters are already fully documented. The description adds contextual value by tying the call to purchase_credits and mentioning proof of ownership, but it does not need to restate the parameter details. This meets the baseline for high schema coverage.

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?

The description states a specific action: 'Check the payment status of a Lightning invoice.' It further anchors the tool in a concrete workflow by saying 'Call after paying the invoice from purchase_credits', which distinguishes it from sibling status/check tools like check_balance, check_price, and check_proof_status.

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 description gives explicit when-to-use guidance: 'Call after paying the invoice from purchase_credits.' It also adds practical constraints such as being free and requiring proof of npub ownership. It does not explicitly name alternatives or exclusions, but the workflow placement is clear enough.

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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TDQS

B3.4/5.0
Disambiguation4/5

Most tools target clearly distinct resources—patron balances vs. operator Authority balances, design text vs. full design fetches, operator vs. patron credentials—and the verbose descriptions carefully separate request/receive flows. A few pairs could still be confused at a glance, such as service_status/session_status, forget_coupon/delete_coupon, and get_design_text/fetch_design.

Naming Consistency4/5

All tools share the roastify_ snake_case prefix and mostly follow a verb_noun pattern like list_, get_, update_, delete_, and create. However, several noun-only names (service_status, session_status, account_statement, oracle_about) and the inconsistent forget_ vs. delete_ distinction for credential/coupon removal keep it from being fully consistent.

Tool Count1/5

69 tools is an extreme count for a single MCP surface and far exceeds the 25+ threshold. Even though the tools span many subdomains—design, payments, coupons, credentials, pricing, notarization, oracle—the sheer number will be heavy on agent context and selection accuracy.

Completeness3/5

Core workflows are largely covered: design storage/editing, coupon lifecycle, credential vaults, credit purchasing, pricing models, notarization, and the oracle all have reasonable read/write surfaces. However, session_status explicitly tells not_registered operators to call register_operator, which does not exist in the tool set, and design editing lacks any delete-element operation, leaving notable dead ends.