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generate_column_chart

Read-only

Create column charts to compare categorical data values using height-based visualizations, supporting grouping, stacking, and customizable styling options.

Instructions

Generate a column chart, which are best for comparing categorical data, such as, when values are close, column charts are preferable because our eyes are better at judging height than other visual elements like area or angles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesData for column chart, such as, [{ category: 'Category A', value: 10 }, { category: 'Category B', value: 20 }], when grouping or stacking is needed for column, the data should contain a 'group' field, such as, [{ category: 'Beijing', value: 825, group: 'Gas Car' }, { category: 'Beijing', value: 1000, group: 'Electric Car' }].
groupNoWhether grouping is enabled. When enabled, column charts require a 'group' field in the data. When `group` is true, `stack` should be false.
stackNoWhether stacking is enabled. When enabled, column charts require a 'group' field in the data. When `stack` is true, `group` should be false.
styleNoStyle configuration for the chart with a JSON object, optional.
themeNoSet the theme for the chart, optional, default is 'default'.default
widthNoSet the width of chart, default is 600.
heightNoSet the height of chart, default is 400.
titleNoSet the title of chart.
axisXTitleNoSet the x-axis title of chart.
axisYTitleNoSet the y-axis title of chart.

Implementation Reference

  • Generic handler function for all chart generation tools, including 'generate_column_chart'. Maps the tool name to the chart type 'column' via CHART_TYPE_MAP, validates input arguments using the specific schema from Charts.column.schema, generates the chart URL by calling generateChartUrl, and returns the MCP-formatted response with the URL and spec.
    export async function callTool(tool: string, args: object = {}) {
      logger.info(`Calling tool: ${tool}`);
      const chartType = CHART_TYPE_MAP[tool as keyof typeof CHART_TYPE_MAP];
    
      if (!chartType) {
        logger.error(`Unknown tool: ${tool}`);
        throw new McpError(ErrorCode.MethodNotFound, `Unknown tool: ${tool}.`);
      }
    
      try {
        // Validate input using Zod before sending to API.
        // Select the appropriate schema based on the chart type.
        const schema = Charts[chartType].schema;
    
        if (schema) {
          // Use safeParse instead of parse and try-catch.
          const result = z.object(schema).safeParse(args);
          if (!result.success) {
            logger.error(`Invalid parameters: ${result.error.message}`);
            throw new McpError(
              ErrorCode.InvalidParams,
              `Invalid parameters: ${result.error.message}`,
            );
          }
        }
    
        const isMapChartTool = [
          "generate_district_map",
          "generate_path_map",
          "generate_pin_map",
        ].includes(tool);
    
        if (isMapChartTool) {
          // For map charts, we use the generateMap function, and return the mcp result.
          const { metadata, ...result } = await generateMap(tool, args);
          return result;
        }
    
        const url = await generateChartUrl(chartType, args);
        logger.info(`Generated chart URL: ${url}`);
    
        return {
          content: [
            {
              type: "text",
              text: url,
            },
          ],
          _meta: {
            description:
              "This is the chart's spec and configuration, which can be renderred to corresponding chart by AntV GPT-Vis chart components.",
            spec: { type: chartType, ...args },
          },
        };
        // biome-ignore lint/suspicious/noExplicitAny: <explanation>
      } catch (error: any) {
        logger.error(
          `Failed to generate chart: ${error.message || "Unknown error"}.`,
        );
        if (error instanceof McpError) throw error;
        if (error instanceof ValidateError)
          throw new McpError(ErrorCode.InvalidParams, error.message);
        throw new McpError(
          ErrorCode.InternalError,
          `Failed to generate chart: ${error?.message || "Unknown error."}`,
        );
      }
    }
  • Zod-based input schema definition for the generate_column_chart tool, specifying data structure, grouping/stacking options, styling, theme, dimensions, and axis titles.
    const schema = {
      data: z
        .array(data)
        .describe(
          "Data for column chart, such as, [{ category: 'Category A', value: 10 }, { category: 'Category B', value: 20 }], when grouping or stacking is needed for column, the data should contain a 'group' field, such as, [{ category: 'Beijing', value: 825, group: 'Gas Car' }, { category: 'Beijing', value: 1000, group: 'Electric Car' }].",
        )
        .nonempty({ message: "Column chart data cannot be empty." }),
      group: z
        .boolean()
        .optional()
        .default(true)
        .describe(
          "Whether grouping is enabled. When enabled, column charts require a 'group' field in the data. When `group` is true, `stack` should be false.",
        ),
      stack: z
        .boolean()
        .optional()
        .default(false)
        .describe(
          "Whether stacking is enabled. When enabled, column charts require a 'group' field in the data. When `stack` is true, `group` should be false.",
        ),
      style: z
        .object({
          backgroundColor: BackgroundColorSchema,
          palette: PaletteSchema,
          texture: TextureSchema,
        })
        .optional()
        .describe(
          "Style configuration for the chart with a JSON object, optional.",
        ),
      theme: ThemeSchema,
      width: WidthSchema,
      height: HeightSchema,
      title: TitleSchema,
      axisXTitle: AxisXTitleSchema,
      axisYTitle: AxisYTitleSchema,
    };
  • Tool descriptor registration for 'generate_column_chart', including name, description, input schema (JSON-converted from Zod), and annotations. Exported as part of the 'column' module for inclusion in Charts.
    const tool = {
      name: "generate_column_chart",
      description:
        "Generate a column chart, which are best for comparing categorical data, such as, when values are close, column charts are preferable because our eyes are better at judging height than other visual elements like area or angles.",
      inputSchema: zodToJsonSchema(schema),
      annotations: {
        title: "Generate Column Chart",
        readOnlyHint: true,
      },
    };
  • src/server.ts:64-77 (registration)
    Registers the MCP tool handlers on the server: listTools returns all enabled chart tools (including generate_column_chart via dynamic Charts lookup), and callTool delegates execution to utils/callTool for the named tool.
    function setupToolHandlers(server: Server): void {
      logger.info("setting up tool handlers...");
      server.setRequestHandler(ListToolsRequestSchema, async () => ({
        tools: getEnabledTools().map((chart) => chart.tool),
      }));
    
      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
      server.setRequestHandler(CallToolRequestSchema, async (request: any) => {
        logger.info("calling tool", request.params.name, request.params.arguments);
    
        return await callTool(request.params.name, request.params.arguments);
      });
      logger.info("tool handlers set up");
    }
  • Mapping from tool names to internal chart types; 'generate_column_chart' maps to 'column' for schema lookup and chart generation.
    const CHART_TYPE_MAP = {
      generate_area_chart: "area",
      generate_bar_chart: "bar",
      generate_boxplot_chart: "boxplot",
      generate_column_chart: "column",
      generate_district_map: "district-map",
      generate_dual_axes_chart: "dual-axes",
      generate_fishbone_diagram: "fishbone-diagram",
      generate_flow_diagram: "flow-diagram",
      generate_funnel_chart: "funnel",
      generate_histogram_chart: "histogram",
      generate_line_chart: "line",
      generate_liquid_chart: "liquid",
      generate_mind_map: "mind-map",
      generate_network_graph: "network-graph",
      generate_organization_chart: "organization-chart",
      generate_path_map: "path-map",
      generate_pie_chart: "pie",
      generate_pin_map: "pin-map",
      generate_radar_chart: "radar",
      generate_sankey_chart: "sankey",
      generate_scatter_chart: "scatter",
      generate_treemap_chart: "treemap",
      generate_venn_chart: "venn",
      generate_violin_chart: "violin",
      generate_waterfall_chart: "waterfall",
      generate_word_cloud_chart: "word-cloud",
    } as const;
Behavior3/5

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

Annotations provide readOnlyHint=true, indicating this is a safe operation. The description doesn't contradict this. It adds some behavioral context about visual perception ('our eyes are better at judging height'), but doesn't disclose important operational details like what format the output takes (image URL, base64, chart object), performance characteristics, or any limitations. With annotations covering safety, the description adds minimal but not misleading context.

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

Conciseness3/5

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

The description is a single run-on sentence that mixes purpose statements with visual perception theory. It's not well-structured or front-loaded with essential information. While not excessively long, it's not efficiently organized, with the visual perception explanation not clearly earning its place for tool selection purposes.

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

Completeness2/5

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

For a complex visualization tool with 10 parameters, nested objects, and no output schema, the description is inadequate. It doesn't explain what the tool returns (critical for a chart generation tool), doesn't provide examples of typical use cases, and doesn't address common pitfalls. With rich schema but no output schema, the description should compensate more than it does.

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 description coverage is 100%, so the schema already documents all 10 parameters thoroughly. The description provides no additional parameter information beyond what's in the schema. It doesn't explain the relationship between parameters or provide usage examples. Baseline 3 is appropriate when the schema does all the parameter documentation work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Generate a column chart' which is tautological with the tool name. It provides some context about when column charts are best used (comparing categorical data, values close), but doesn't clearly articulate what the tool actually produces (e.g., an image, chart object, visualization). The purpose is vague beyond restating the name.

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

Usage Guidelines2/5

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

The description mentions that column charts are 'best for comparing categorical data' and 'when values are close', which provides some general chart selection guidance. However, it doesn't explicitly state when to use this tool versus its many siblings (e.g., bar_chart, line_chart, pie_chart) or provide clear alternatives. No explicit when-not-to-use guidance is provided.

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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