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Glama

tollbooth-sample

by io.github.lonniev

weather_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. Changed1 schema field changed
    • changedInput schema / properties / dpop_token / description
      Previous value: -"A kind-27235 Nostr event signed by npub for this tool."New value: +"Raw JSON of a kind-27235 Nostr event signed by npub —\nnot base64, not NIP-98 'Authorization: Nostr <b64>' framing. Its\n`u` tag must hold THIS tool's exact name (from tools/list), not\nthe endpoint URL; content:\"\", created_at within 60s of now, and a\nrandom `nonce` tag recommended. Or a cached dpop_token phrase."
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that the tool is free, requires proof of npub ownership, and explains the reason (anti-front-running). This is good behavioral context, though it does not detail side effects or return 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 extremely concise—four short lines—with no wasted words. It front-loads the purpose and flows logically.

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?

The description covers purpose, usage timing, cost, and authentication requirement. Given the tool's simplicity (3 params, no nested objects, output schema present), it is fully complete and leaves no gaps.

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?

Input schema has 100% coverage with detailed descriptions for each parameter. The description adds context about when to call (after purchase_credits) but does not provide additional meaning beyond the schema. Baseline of 3 is appropriate.

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 clearly states the tool's purpose: 'Check the payment status of a Lightning invoice.' It specifies the exact action (check) and the resource (payment status), and distinguishes itself from siblings by linking to purchase_credits.

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 explicitly says 'Call after paying the invoice from purchase_credits' and notes it is free, providing clear when-to-use guidance. It does not mention alternatives or when not to use, which would improve clarity further.

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 have clearly distinct purposes with detailed descriptions, and the few similar pairs (e.g., check_balance vs check_authority_balance, account_statement vs account_statement_infographic) are well-differentiated by their descriptions. However, some overlapping concepts like forget_credentials vs delete_patron_credential/delete_operator_credential could still cause misselection without careful reading.

Naming Consistency2/5

All tools share the misleading 'weather_' prefix, which does not reflect their actual domain (billing, credentials, coupons, notarization). Naming patterns are inconsistent, mixing verb_noun (check_balance, list_coupons) with noun-ish names (account_statement, current, forecast) and varied verbs (get, list, check, request, receive, update, delete, forget, mint, redeem, etc.).

Tool Count1/5

With 52 tools, this is an extremely large surface for a sample server. Even though the domain is broad, this count far exceeds the typical well-scoped MCP server and creates unnecessary complexity for agents to navigate.

Completeness4/5

The tollbooth/billing domain is well-covered: credit purchasing, coupons, credentials, proofs, pricing models, notarization, and operator/patron status. Minor gaps exist (e.g., no single-coupon getter, no direct patron list), but core workflows have no dead ends. The weather aspect is thin with only three tools, but that seems intentional as an example paid service.

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