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@zients/line-mcp-server

@zients/line-mcp-server

by zients
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TypeScript
Remote

Get Statistics Per Unit

get_statistics_per_unit

Retrieve message statistics per custom aggregation unit for a specified date range to analyze messaging performance.

Instructions

Get statistics per custom aggregation unit for messages sent within a specified date range.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "customAggregationUnit": "<customAggregationUnit>",
      "from": "<from>",
      "to": "<to>"
    },
    "name": "get_statistics_per_unit"
  }
}

Input Schema

NameRequiredDescriptionDefault
toYesEnd date in yyyyMMdd format (e.g. 20240131). Timezone: UTC+9
fromYesStart date in yyyyMMdd format (e.g. 20240101). Timezone: UTC+9
customAggregationUnitYesName of the custom aggregation unit

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

C2.7/5.0
Behavior1/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the action without mentioning side effects, permissions, rate limits, pagination, or return format. The agent is left uninformed about operational constraints or expected output structure.

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 a single sentence with no redundant words, front-loading the core action and scope. It is maximally concise while still conveying the essential purpose, earning a top score for efficiency.

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

Completeness2/5

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

Given the absence of an output schema and annotations, the description is too sparse to be complete. It does not explain what statistics are returned, how the date range behaves, or any edge cases. An agent would need more context to confidently invoke the tool and interpret results.

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?

Schema coverage is 100%, so all three parameters have descriptions. The tool description adds no additional semantic meaning beyond the schema, such as date format nuances or the meaning of a custom aggregation unit. Baseline 3 is appropriate since the schema already documents the parameters.

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 clearly states a specific action (Get), a resource (statistics per custom aggregation unit), and a scope (messages within a date range). It distinguishes itself from siblings like push or user-profile tools, though the term 'statistics' is somewhat generic and doesn't specify which metrics are returned.

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?

The description provides no guidance on when to use this tool versus alternatives such as get_sent_push_count or get_message_deliveries. It neither names alternatives nor explains why this tool is preferable for aggregate statistics, leaving the agent to infer context.

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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