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CanvasXpress MCP Server

by neuhausi

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    • F
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      Generates CanvasXpress data visualization JSON configurations from natural language descriptions using RAG with 132 few-shot examples, achieving 93% exact match accuracy for creating charts and plots.
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    • A
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      B
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      Enables agents to turn structured tabular data or CSV/XLSX files into interactive charts that render inline in conversations, with client-adaptive output modes and self-contained HTML.
      MIT
    • F
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      quality
      B
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      Turns a table your coding agent already has — a CSV, dataframe or semantic-model query result — into chart code that fits it first try, by profiling the data locally, ranking roughly 90 chart types by what it can honestly show, and returning verified D3, Plotly or matplotlib source you own and can run anywhere. It can also find the natural views inside a Microsoft Fabric semantic model and say which chart suits each.
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