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monacloud-mcp

monacloud-mcp

Official
by themonagroup
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Thống kê webhook

monapay_webhook_stats

Track webhook delivery success rate, P95 latency, and error categories to identify and troubleshoot delivery failures.

Instructions

Tỷ lệ thành công, P95, phân loại lỗi. / Webhook delivery stats.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "monapay_webhook_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.2

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations and no output schema, the description carries full responsibility for behavioral disclosure. It does reveal the key output dimensions (success rate, P95, error classification), which is meaningful. However, it omits important traits such as whether the data is read-only, what time range is covered, whether it aggregates all webhooks or a subset, and what the response structure looks like.

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: a short Vietnamese phrase listing the metrics followed by an English translation. Every word adds value, and the key information is front-loaded. There is no repetition or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter stats tool, the description names the three most important metrics, but it leaves gaps around the aggregation period, scope (which webhooks are included), and the exact output format. Since there is no output schema or annotation, these omissions could lead an agent to misinterpret the data or make wrong assumptions about the returned payload.

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

Parameters4/5

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

The input schema is empty and there are zero parameters, so there are no parameter semantics to document. The baseline of 4 applies because the description does not need to add parameter meaning, and it doesn't introduce any confusing parameter-related text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific resource (webhook stats) and lists concrete metrics (success rate, P95, error classification), which clearly communicates what the tool provides. It also differentiates from sibling tools like monapay_webhook_logs, which focus on raw logs. However, it uses a noun phrase ('Webhook delivery stats') rather than an explicit verb like 'get' or 'retrieve'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No usage guidance is provided. The description does not explain when to choose this tool over alternatives such as monapay_webhook_logs or monapay_email_stats, nor does it mention any prerequisites or filtering context. An agent must infer usage entirely from the name and metric list.

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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