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kkawailab

MLIT Data Platform MCP Server

by kkawailab

get_data_summary

Retrieve basic information like title and ID for specific datasets from Japan's MLIT Data Platform using dataset and data IDs, ideal for quick verification before detailed data access.

Instructions

データセットIDとデータIDを用いて、基本情報(データID、タイトル)を取得する。

            使い方:
            - すでに dataSetID / dataID を把握している場合に、軽量にタイトル等の基本情報だけ取得します。
            - 検索結果から拾った id を入れて確認・プレビュー用途に最適。

            例:
            - タイトルだけ確認したい:
            dataset_id="cals_construction", data_id="<searchで取得したid>"

            - 詳細取得前の事前チェック:
            dataset_id="mlit-001", data_id="xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"

            注意:
            - data_id は検索API(search)の結果で得られる DataClass.id を使用してください。
            - 指定したIDに一致しない場合、totalNumber=0 となります(結果なし)。
            - サマリ用途のため、詳細な付帯情報が必要な場合は get_data を使用してください。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYesデータセットID。searchツールの結果から取得
data_idYesデータID。searchツールの結果から取得

Implementation Reference

  • The 'get_data_summary' method in MLITClient defines the GraphQL query and executes it via post_query.
    async def get_data_summary(self, dataset_id: str, data_id: str) -> Dict[str, Any]:
        q = f"""
        query {{
          data(dataSetID: "{dataset_id}", dataID: "{data_id}") {{
            totalNumber
            getDataResults {{
              id
              title
            }}
          }}
        }}
        """.strip()
        return await self.post_query(q)
  • src/server.py:339-371 (registration)
    Definition of the 'get_data_summary' tool in the server catalog.
        name="get_data_summary",
        description="""データセットIDとデータIDを用いて、基本情報(データID、タイトル)を取得する。
    
            使い方:
            - すでに dataSetID / dataID を把握している場合に、軽量にタイトル等の基本情報だけ取得します。
            - 検索結果から拾った id を入れて確認・プレビュー用途に最適。
    
            例:
            - タイトルだけ確認したい:
            dataset_id="cals_construction", data_id="<searchで取得したid>"
    
            - 詳細取得前の事前チェック:
            dataset_id="mlit-001", data_id="xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
    
            注意:
            - data_id は検索API(search)の結果で得られる DataClass.id を使用してください。
            - 指定したIDに一致しない場合、totalNumber=0 となります(結果なし)。
            - サマリ用途のため、詳細な付帯情報が必要な場合は get_data を使用してください。""",
        inputSchema={
            "type": "object",
            "properties": {
                "dataset_id": {
                    "type": "string",
                    "description": "データセットID。searchツールの結果から取得"
                },
                "data_id": {
                    "type": "string",
                    "description": "データID。searchツールの結果から取得"
                }
            },
            "required": ["dataset_id", "data_id"],
        },
    ),
  • The handle_call_tool implementation that invokes 'get_data_summary' on the client.
    elif name == "get_data_summary":
        p = GetDataParams.model_validate(arguments)
        data = await client.get_data_summary(p.dataset_id, p.data_id)
Behavior4/5

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

With no annotations provided, description carries full burden. It discloses the 'totalNumber=0' behavior when IDs don't match, clarifies this returns only basic info vs full details, and explains the lightweight nature of the operation. Lacks explicit read-only/safety declaration but implies it through context.

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?

Excellent structure with clearly delineated sections (purpose, usage, examples, notes). Japanese formatting uses line breaks effectively. Every sentence provides distinct value - no redundancy.

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

Completeness5/5

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

For a 2-parameter lookup tool with no output schema, description is complete. It explains return content (basic info), error cases, prerequisites (search API), and sibling alternatives. No gaps given the tool's simplicity.

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?

Despite 100% schema coverage (baseline 3), description adds concrete value through usage examples showing actual ID formats (e.g., 'cals_construction', 'mlit-001', UUID pattern) and reinforces that IDs come from search tool results.

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?

Description explicitly states it retrieves basic information (data ID, title) using dataset and data IDs. It clearly distinguishes from sibling tool get_data by stating this is for summary/basic info while get_data is for detailed information.

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

Usage Guidelines5/5

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

Contains explicit '使い方' (usage) section describing when to use (when IDs are already known, for lightweight retrieval, for preview). Explicitly names search API as prerequisite for obtaining IDs and get_data as alternative for detailed information.

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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