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togaf10_EAP_MCP

togaf10_EAP_MCP

by Waganawa-Megumin
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Get the dashboard as Markdown

get_dashboard

Retrieve the current engagement as a Markdown dashboard for chat, copying, or printing. Automatically trims large tables to top rows, indicating when trimming occurs.

Instructions

現在の案件の状態を Markdown ダッシュボードで返す(会話内表示・コピペ・印刷向け)。件数が多いと各表を上位のみに絞り、絞った旨を表示する。 / Return the current engagement as a Markdown dashboard for chat, copy, and print. Large tables are trimmed to their top rows, always saying so.

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": "get_dashboard"
  }
}

Input Schema

NameRequiredDescriptionDefault
langNo出力言語 / Output languageboth
limitNo1 表あたりの件数(既定 20、compact=true なら 5)。指定でその件数に絞る / Rows per table (default 20; 5 when compact=true).
compactNo各表を上位のみに絞る。未指定なら多いときだけ自動、false で全件 / Trim each table to its top rows; omit for automatic, false for every row.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the disclosure burden. It honestly explains that large tables are automatically trimmed to top rows and that the tool always indicates when trimming occurs. This is meaningful behavioral context beyond 'returns a dashboard', though it does not address edge cases like missing current engagement.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded, leading with the core action and format. The bilingual duplication is justifiable in context and does not bloat the entry, though it slightly reduces efficiency.

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

Completeness4/5

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

For a low-complexity tool with three optional parameters and no output schema, the description adequately explains output format, intended audience, and the key trimming behavior. It does not mention behavior when no current engagement is active, but this is a minor gap given the overall clarity.

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%, with each parameter already having a clear description including defaults and the compact behavior. The tool description adds little beyond the schema, so the baseline of 3 is appropriate.

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?

The description states a specific verb ('Return'), a resource ('the current engagement'), and a deliverable format ('Markdown dashboard'). It clearly distinguishes itself from siblings like get_engagement, open_dashboard, and export_report by framing the output as a chat-friendly, print-friendly status view.

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?

The description provides clear context: this is for displaying current engagement status in chat, copy-paste, or print scenarios. It does not explicitly name alternatives or state when not to use it, but the intended use case is clear enough for an agent to route appropriately.

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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