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    | 地価トレンド予測 | 新宿区の5年後地価をAI予測。CAGR・投資シグナル付き | | 企業立地需要分析 | 名古屋市中区のオフィス・工場需要スコアを算出 | | ファミリー向け適性評価 | 横浜市西区の教育・安全・医療スコアを総合評価 | | ポートフォリオ最適化 | 東京・大阪・埼玉の3エリアに投資配分を最適化 | | What-If シナリオ分析 | 大阪市中央区で新駅開設シナリオを試算 | | 店舗出店適地評価 | 福岡市博多区の人流・商業施設・交通データで出店適性を判定 |
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    AGPL 3.0
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    Provides football analytics tools including player scouting, comparisons, market-value filters, expected-goals tables, match-by-match form, team attacking profiles, match search, shot maps, and more for 10 leagues.
    6
    GPL 3.0
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    Enables scientific data introspection and visualization of VTK datasets with format-adaptive metadata extraction and interactive 3D visualization through MCP tools.
    1
    MIT
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    Enables data analysis and visualization operations such as loading CSV/Excel/JSON files, computing summary statistics, generating charts, and exploring datasets via SSE/HTTP.
    MIT
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    A real MCP server exposing data-analyst tools (SQL querying, schema introspection, stats, charting) over a sales database, with a Gemini agent that discovers and calls those tools at runtime via the standard MCP protocol.
    MIT
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    A natural-language data analyst MCP server that lets users query SQLite sales datasets via MCP tools (list_tables, aggregate, time_series, run_sql) with read-only SQL safety guards, returning results through a FastAPI dashboard.
    MIT
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    A safety-gated Python MCP server for Metabase that lets MCP clients and agents inspect Metabase, run governed queries, and create or maintain dashboards, cards/questions, collections, snippets, permissions, and other Metabase assets through the Metabase REST API.
    GPL 3.0
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    A clean-room SHARP-on-MCP compliant FHIR R4 MCP server that enables AI agents to interact with any FHIR R4 endpoint using SHARP context headers, without server-side OAuth. It provides clinical tools, lab results, imaging, and interactive MCP-UI dashboards.
    MIT
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    A production-grade MCP server that provides tools to fetch historical stock data, company information, and technical indicators (SMA, EMA, RSI, MACD, Bollinger Bands) via yfinance, and includes a Flask web dashboard for visual analysis.
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    An MCP server that answers natural-language questions over CSV, Excel, and SQL data by providing deterministic tools for loading, profiling, querying, cleaning, statistical analysis, visualization, and reporting. It enables LLMs to plan and interpret while all computation is done exactly through MCP tools.
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    Enables LLMs to generate visual summary reports from CSV datasets using MCP Resources, Tools, and Prompts, without exposing raw data to the model.
    10