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Glama

Indonesia Payments (Midtrans Snap — GoPay / QRIS / Alfamart / bank VA)

Server Details

Indonesia payments for AI agents — GoPay, QRIS, Alfamart, bank VA via Midtrans. Never holds funds.

Status
Healthy
Last Tested
Transport
Streamable HTTP
URL

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Tool DescriptionsA

Average 4.7/5 across 2 of 2 tools scored.

Server CoherenceA
Disambiguation5/5

Only two tools with clearly distinct purposes: one creates a payment link, the other queries its status. No overlap or ambiguity.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern: create_payment_link and query_payment_status, both using snake_case.

Tool Count4/5

Two tools is minimal but reasonable for a focused integration covering payment link creation and status query. Slightly under but not extreme.

Completeness3/5

Core workflow (create and check) is covered, but missing essential operations like cancel, refund, or list transactions, which limits agent capabilities.

Available Tools

2 tools
query_payment_statusA
Read-only
Inspect

Check whether an Indonesia payment (created by create_payment_link) has been paid. Fetches the transaction status from Midtrans directly — a reliable pull-based alternative to webhooks. paid=true when transaction_status is settlement (or capture with fraud_status accept).

ParametersJSON Schema
NameRequiredDescriptionDefault
order_idYesThe order_id returned by create_payment_link
providerNoOmit to auto-select by credential headers.
Behavior5/5

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

Annotations already provide readOnlyHint and openWorldHint. Description adds value by explaining pull-based nature, direct Midtrans fetch, and specific condition for paid=true. No contradictions.

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?

Two efficient sentences, front-loaded with purpose. No redundant words. Every sentence adds value.

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?

Despite no output schema, description explains the return condition (paid=true logic). Covers key behavior for a simple read tool. Complete for the tool's complexity.

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

Parameters5/5

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

Schema coverage is 100% and description adds meaning: order_id is from create_payment_link, provider can be omitted for auto-selection. Enhances understanding beyond schema.

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?

Clearly states the tool checks payment status for Indonesia payments created by create_payment_link, with specific verb 'check' and resource 'payment status'. Distinguishes from sibling tool by focusing on status checking vs. creation.

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?

Explicitly describes when to use: as a pull-based alternative to webhooks. Not explicit about when not to use, but context implies it's for after payment link creation. Lacks exclusion scenarios but provides clear use case.

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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