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ETFフローMCP

AI エージェントの意思決定を強化するために暗号通貨 ETF フロー データを配信する MCP サーバー。

不和 パイソンライセンス状態

特徴

  • 統合ツール: get_etf_flowツールは、BTC または ETH の過去の ETF フロー データを動的に取得します。

  • マークダウン テーブル出力: ピボット テーブルを活用して、ETF ティッカーを列、日付を行、合計フローの合計列でデータを表示します。

  • プロンプト ガイダンス: LLM のインタラクションを合理化してユーザー フレンドリなクエリを実現するプロンプト ( etf_flow_prompt ) が含まれています。

Related MCP server: crypto-portfolio-mcp

前提条件

  • Python : バージョン 3.10 以上。

  • uv : 高速な Python パッケージおよびプロジェクト マネージャー (インストール手順)。

  • CoinGlass API キー: CoinGlassからキーを取得します。

  • Claude Desktop : オプション、インタラクティブなクエリ用。

  • Git : リポジトリのクローンを作成します。

インストール

  1. リポジトリのクローンを作成します:

    git clone https://github.com/kukapay/etf-flow-mcp.git
    cd etf-flow-mcp
  2. uv でセットアップ: uvを使用して依存関係をインストールします:

    uv sync

使用法

Claude Desktopとの統合

  1. Claude デスクトップを構成する: claude_desktop_config.json (macOS の場合は~/Library/Application Support/Claude 、Windows の場合は%APPDATA%\Claudeあります) にサーバーを追加します。

    {
      "mcpServers": {
        "etf-flow-mcp": {
          "command": "uv",
          "args": ["--directory", "/absolute/path/to/etf-flow-mcp", "run", "etf-flow-mcp"],
          "env": { "COINGLASS_API_KEY": "your_coinglass_api_key_here" }
        }
      }
    }

    /absolute/path/to/etf-flow-mcp/cli.pyをcli.pyへのフルパスに置き換えます。

  2. Claude Desktop を再起動します。Claude Desktop UI にハンマー アイコンが表示され、サーバーがロードされていることを確認します。

  3. クエリ例:

    • 「最新のBTC ETFフローデータを表で表示してください」

    • 「ETH ETFのフロー履歴を取得する」

出力例

  • BTC ETFフロー:

    | Date       | GBTC      | IBIT      | FBTC      | ARKB      | BITB      | BTCO     | HODL     | BRRR     | EZBC     | BTCW     | Total     |
    |------------|-----------|-----------|-----------|-----------|-----------|----------|----------|----------|----------|----------|-----------|
    | 2025-04-24 | 0         | 327300000 | 0         | 97700000  | 10200000  | 7750000  | 0        | 0        | 0        | 0        | 442200000 |
    | 2025-04-23 | 0         | 643200000 | 124400000 | 129500000 | -15200000 | 0        | 5300000  | 0        | 0        | 0        | 917700000 |
    | 2025-04-22 | 65100000  | 193500000 | 253800000 | 267100000 | 76700000  | 18300000 | 6500000  | 0        | 10600000 | 0        | 912700000 |
    | 2025-04-21 | 36600000  | 41600000  | 88100000  | 116100000 | 45100000  | 0        | 11700000 | 0        | 10100000 | 0        | 381300000 |
    | 2025-04-18 | 0         | 0         | 0         | 0         | 0         | 0        | 0        | 0        | 0        | 0        | 0         |
  • ETH ETFフロー:

    | Date       | ETHE      | GETH     | ETHA      | ETHW     | FETH      | ETHV     | EZET     | CETH     | QETH     | Total     |
    |------------|-----------|----------|-----------|----------|-----------|----------|----------|----------|----------|-----------|
    | 2025-04-24 | -6600000  | 18300000 | 40000000  | 5100000  | 0         | 2600000  | 0        | 4100000  | 0        | 63550000  |
    | 2025-04-23 | 0         | 6400000  | -30300000 | 0        | 0         | 0        | 0        | 0        | 0        | -23900000  |
    | 2025-04-22 | 0         | 0        | 0         | 6100000  | 32700000  | 0        | 0        | 0        | 0        | 38800000  |
    | 2025-04-21 | -25400000 | 0        | 0         | 0        | 0         | 0        | 0        | 0        | 0        | -25400000  |
    | 2025-04-18 | 0         | 0        | 0         | 0        | 0         | 0        | 0        | 0        | 0        | 0         |
    | 2025-04-17 | 0         | 0        | 0         | 0        | 0         | 0        | 0        | 0        | 0        | 0         |

ライセンス

このプロジェクトはMIT ライセンスに基づいてライセンスされています。

Available Tools

1 tool
get_etf_flowA
Fetch historical ETF flow data for BTC or ETH from CoinGlass API and return as a Markdown table.

Parameters:
    coin (str): Cryptocurrency to query ('BTC' or 'ETH').

Returns:
    str: Markdown table with ETF flow data (tickers as columns, dates as rows, with total column).
ParametersJSON Schema
NameRequiredDescriptionDefault
coinYes

TDQS

A4.1/5.0
Behavior3/5

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 describes the action ('Fetch') and output format ('Markdown table'), but lacks details on error handling, rate limits, authentication needs, or data freshness. It adequately covers basic behavior but misses advanced operational 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?

The description is well-structured and front-loaded, with a clear opening sentence followed by specific sections for parameters and returns. Every sentence adds value without redundancy, making it efficient and easy to parse.

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?

Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, parameters, and return format, but could improve by addressing behavioral aspects like error cases or data limitations, which would enhance completeness for an API-based tool.

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

Parameters5/5

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

The description adds significant meaning beyond the input schema, which has 0% coverage. It explicitly defines the 'coin' parameter as a string with allowed values ('BTC' or 'ETH') and explains its purpose ('Cryptocurrency to query'), compensating fully for the schema's lack of documentation.

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 clearly states the tool's purpose with a specific verb ('Fetch'), resource ('historical ETF flow data'), and scope ('for BTC or ETH from CoinGlass API'). It distinguishes the data source and format, making the function unambiguous even without sibling tools.

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

Usage Guidelines3/5

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

The description implies usage context by specifying the cryptocurrency options ('BTC' or 'ETH') and the data source (CoinGlass API), but it does not provide explicit guidance on when to use this tool versus alternatives or any prerequisites. Since there are no sibling tools, the lack of comparative guidance is less critical.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedget_etf_flow

TDQS

A3.9/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is singular and clearly defined.

Naming Consistency5/5

The single tool name 'get_etf_flow' follows a clear verb_noun pattern. Since there are no other tools, consistency is inherently perfect.

Tool Count2/5

One tool is too few for the server's apparent scope of ETF flow data analysis. It lacks complementary tools like historical trends, comparisons, or metadata, making the surface feel thin and incomplete for the domain.

Completeness2/5

The tool set is severely incomplete for ETF flow analysis. It only fetches data for BTC or ETH, missing operations like multi-coin queries, date range filtering, summary statistics, or visualization, which are essential for comprehensive coverage.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

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