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generate_word_cloud_chart

Read-only

Create word cloud charts to visualize word frequency or importance through text size variation. Analyze common terms in social media, reviews, or feedback data.

Instructions

Generate a word cloud chart to show word frequency or weight through text size variation, such as, analyzing common words in social media, reviews, or feedback.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesData for word cloud chart, it should be an array of objects, each object contains a `text` field and a `value` field, such as, [{ value: 4.272, text: '形成' }].
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.

Implementation Reference

  • MCP server request handler for CallToolRequestSchema. This is the entry point for executing the generate_word_cloud_chart tool, dispatching to callTool function.
    // 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);
    });
  • Core handler function for chart tools. For 'generate_word_cloud_chart', maps to chartType 'word-cloud', validates input with word-cloud schema, calls generateChartUrl to produce chart, returns URL.
    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 for the generate_word_cloud_chart tool, defining data (array of {text, value}), optional style, theme, width, height, title.
    const schema = {
      data: z
        .array(data)
        .describe(
          "Data for word cloud chart, it should be an array of objects, each object contains a `text` field and a `value` field, such as, [{ value: 4.272, text: '形成' }].",
        )
        .nonempty({ message: "Word cloud chart data cannot be empty." }),
      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,
    };
  • Tool descriptor defining name, description, inputSchema (converted to JSON schema), and annotations for generate_word_cloud_chart.
    const tool = {
      name: "generate_word_cloud_chart",
      description:
        "Generate a word cloud chart to show word frequency or weight through text size variation, such as, analyzing common words in social media, reviews, or feedback.",
      inputSchema: zodToJsonSchema(schema),
      annotations: {
        title: "Generate Word Cloud Chart",
        readOnlyHint: true,
      },
    };
  • Mapping of tool names to internal chart types, registering 'generate_word_cloud_chart' to 'word-cloud' for processing.
    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 read operation. The description adds value by explaining the chart's purpose (visualizing word frequency/weight) and giving example use cases, which goes beyond the annotations. However, it doesn't disclose additional behavioral traits like output format (e.g., image URL, base64), performance characteristics, or error handling. No contradiction with annotations exists.

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

Conciseness4/5

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

The description is a single, well-structured sentence that efficiently states the purpose and provides an example use case. It's appropriately sized without unnecessary fluff, though it could be slightly more front-loaded by explicitly naming the tool's output (e.g., 'generates a chart image'). Every sentence earns its place.

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

Completeness3/5

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

Given the tool's moderate complexity (6 parameters, nested objects) and rich schema coverage (100%), the description is adequate but minimal. It explains the 'what' and 'why' but lacks details on output (no output schema provided), error conditions, or advanced usage. With annotations covering safety, it meets minimum viability but leaves gaps for an agent to fully understand behavioral context.

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 fully documents all parameters. The description adds no specific parameter information beyond what's in the schema. It mentions 'word frequency or weight' which loosely relates to the 'data' parameter but doesn't provide additional syntax, constraints, or examples beyond the schema. Baseline 3 is appropriate given high schema coverage.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Generate a word cloud chart to show word frequency or weight through text size variation.' It specifies the verb ('generate') and resource ('word cloud chart') with a functional explanation of how it works. However, it doesn't explicitly differentiate from sibling tools beyond mentioning 'word cloud' specifically, which is implied but not stated as a distinction.

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 provides implied usage context through the example 'such as, analyzing common words in social media, reviews, or feedback,' which suggests appropriate use cases. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., other chart types in the sibling list) or any prerequisites or exclusions. The guidance is helpful but not comprehensive.

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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