United States Payments (Stripe — Stripe checkout)
Server Details
United States payments for AI agents — Stripe checkout via Stripe. Never holds funds.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.5/5 across 2 of 2 tools scored.
The two tools are clearly distinct: one creates a payment link, the other queries payment status. There is no overlap in their purposes.
Both tool names follow a consistent verb_noun pattern using snake_case (create_payment_link, query_payment_status), making them predictable.
With only 2 tools, the server feels thin for the Stripe domain. While it covers creation and status checking, more tools (e.g., cancel, list) would be expected for typical workflows.
The server covers create and status query but lacks essential operations like canceling a payment link, handling refunds, or listing payments. This is a significant gap for a payment system.
Available Tools
2 toolscreate_payment_linkAInspect
Create a payment link in USD for the United States via Stripe (Checkout Sessions). Buyer pays with cards, Apple Pay / Google Pay, and other methods enabled on the Stripe account. Returns a hosted checkout URL the buyer opens to pay — payment completes automatically, no confirm step. Bring your own credentials via HTTP header (x-stripe-secret-key; free test credentials from dashboard.stripe.com never move real money). Money always flows buyer→Stripe→merchant; this service never touches funds.
| Name | Required | Description | Default |
|---|---|---|---|
| amount_usd | Yes | Amount in USD (decimals allowed), e.g. 5.0. Minimum 0.5. | |
| description | Yes | What this payment is for (shown to the buyer, ≤200 chars) | |
| success_url | No | Optional https URL to send the buyer to after payment. | |
| customer_email | No | Optional buyer email. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate create operation (readOnlyHint false), not idempotent, not destructive. Description adds value by explaining payment flow (money never touches service, buyer→Stripe→merchant), no confirm step, and authentication needs, which go beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences, well-structured, and front-loaded with essential information. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains the return value (hosted checkout URL) and covers authentication, payment flow, and money handling, making it 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%; the description repeats some parameter details (e.g., decimals, minimum 0.5, max chars) but does not add significant new meaning beyond what is in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (Create), resource (payment link), currency (USD), country (United States), payment processor (Stripe Checkout Sessions), and what is returned (hosted checkout URL). It also distinguishes from the sibling tool query_payment_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (for US customers in USD) and mentions credential requirements (HTTP header with Stripe secret key), but does not explicitly state when not to use it or mention alternatives beyond the sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_payment_statusARead-onlyInspect
Check whether a the United States payment (created by create_payment_link) has been paid. Queries Stripe directly — pull-based, no webhook needed. paid=true when status is PAID.
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes | The session_id returned by create_payment_link |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and openWorldHint=true. Description adds that it queries Stripe directly and defines the paid condition, providing useful behavioral context without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first clearly states the main action, second adds precise detail about the condition. No unnecessary words, well structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete for a simple one-parameter read tool with strong annotations. Explains the paid field meaning and data source, no gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema already covers session_id with 100% description. Description adds value by specifying it is the ID returned by create_payment_link, linking the parameter to its origin.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool checks whether a US payment has been paid by querying Stripe directly, and specifies the condition for paid=true. It distinguishes from the sibling tool create_payment_link as the verification counterpart.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly mentions pull-based query, no webhook needed, implying when to use it for immediate status checks. Slightly lacks explicit exclusions but provides clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!