Mexico Payments (Mercado Pago — OXXO cash)
Server Details
Mexico payments for AI agents — OXXO cash via Mercado Pago. 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 4.3/5 across 2 of 2 tools scored.
The two tools have completely distinct purposes: one creates a payment link and the other queries its status. There is no overlap or ambiguity between them.
Both tools follow a verb_noun pattern: 'create_payment_link' and 'query_payment_status'. While 'query' is slightly less common than 'get', the pattern is consistent and clear.
With only two tools, the server feels thin for a payment service. While it covers the basic create and check status flows, typical payment services also include refund or cancellation, making this borderline.
The server covers creating a payment link and checking its status, but lacks any update, cancel, or refund capabilities. Missing common payment lifecycle operations reduces completeness.
Available Tools
3 toolscreate_payment_linkAInspect
Create a payment link in MXN for Mexico via Mercado Pago. Buyer pays with OXXO cash vouchers, SPEI bank transfer, cards, Mercado Pago wallet. Returns a hosted checkout URL the buyer opens to pay — payment completes automatically, no confirm step. Bring your own credentials via HTTP header (x-mercadopago-access-token; free test credentials from mercadopago.com developers never move real money). Money always flows buyer→Mercado Pago→merchant; this service never touches funds.
| Name | Required | Description | Default |
|---|---|---|---|
| amount_mxn | Yes | Amount in MXN (decimals allowed), e.g. 100. Minimum 10. | |
| 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. | |
| reference_id | No | Your unique order reference (≤40 chars). Auto-generated if omitted. | |
| customer_email | No | Optional buyer email. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false. The description adds behavioral details: payment completes automatically, no confirm step, need for Mercado Pago access token via HTTP header, and money flow (buyer->Mercado Pago->merchant, service never touches funds). 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph but is front-loaded with the core purpose. Each sentence adds unique information (payment methods, return URL, automatic completion, credentials, money flow). It could be slightly more structured, but it is efficient and avoids redundancy.
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 explicitly states the return value (hosted checkout URL). It covers payment methods, authentication via HTTP header, flow details (automatic completion), and money handling (no touch). The description is comprehensive for the tool's function.
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 coverage is 100% with all parameters having descriptions. The description does not add significant additional semantics beyond the schema; it provides overall context but does not elaborate on individual parameters beyond what's already in the schema. Baseline 3 is appropriate.
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 tool creates a payment link for Mexico via Mercado Pago, listing specific payment methods (OXXO, SPEI, cards, wallet) and returns a hosted checkout URL. It distinguishes itself from the sibling tool 'query_payment_status' which is for querying 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 this tool (to create a payment link for buyers) and mentions important context like automatic completion and no confirm step. It doesn't explicitly state when not to use it or alternative tools, but the sibling tool 'query_payment_status' implies a different use case. Slightly improved by mentioning credential requirements.
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 Mexico payment (created by create_payment_link) has been paid. Queries Mercado Pago directly — pull-based, no webhook needed. paid=true when status is PAID (Mercado Pago APPROVED).
| Name | Required | Description | Default |
|---|---|---|---|
| external_reference | Yes | The external_reference returned by create_payment_link |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable behavioral details: the tool queries the external API directly (real-time) and defines the exact mapping for paid=true (Mercado Pago APPROVED). 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences. The key purpose is front-loaded, and every word adds value without redundancy.
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 primary return indicator (paid=true). For a simple boolean check tool, this is largely sufficient. It could mention other possible statuses, but the current level is adequate.
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 coverage is 100%, so parameters are documented. The description adds context by specifying that 'external_reference' comes from 'create_payment_link', aiding correct invocation.
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 explicitly states the tool's action ('Check whether...has been paid') and clearly identifies the resource ('Mexico payment...created by create_payment_link'). It differentiates from the sibling tool 'create_payment_link' by focusing on status checking rather than creation.
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 the pull-based mechanism ('Queries Mercado Pago directly — pull-based, no webhook needed'), giving clear context for when to use this tool. It lacks explicit when-not-to-use instructions, but the purpose is well-defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
refund_paymentADestructiveInspect
Refund a paid payment (created by create_payment_link). Full refund by default; pass amount for a partial refund where supported. Refunds respect the same owner policy guardrails (x-agentpay-max-amount) as payments — the amount is checked before anything is sent to the gateway.
| Name | Required | Description | Default |
|---|---|---|---|
| amount | No | Optional partial-refund amount in the local currency major unit. Omit for a full refund. | |
| external_reference | Yes | The external_reference of the paid payment (same id used by query_payment_status) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds value beyond annotations by noting the amount is checked before gateway processing and referencing owner policy guardrails (x-agentpay-max-amount).
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 concise sentences that front-load the primary function and default behavior, then add a key constraint. No unnecessary words.
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?
For a simple refund tool with two parameters and no output schema, the description fully covers what the tool does, including default and partial refund options and policy checks.
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 coverage is 100% with parameter descriptions; the tool description mainly reiterates the parameter purpose without adding significant new meaning.
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 ('Refund a paid payment') and the resource ('created by create_payment_link'), distinguishing it from sibling tools that create or query payments.
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?
Specifies default full refund and partial refund via 'amount' parameter, plus mentions policy guardrails. Lacks explicit when-not-to-use but provides sufficient 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!