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scan_text_nodes

Extract all text content from a selected Figma design element to analyze or process text data programmatically.

Instructions

Scan all text nodes in the selected Figma node

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nodeIdYesID of the node to scan

Implementation Reference

  • MCP tool handler for 'scan_text_nodes'. Sends command to Figma plugin to scan text nodes in a given nodeId, supports chunking for large designs, formats and returns the list of text nodes found.
      "scan_text_nodes",
      "Scan all text nodes in the selected Figma node",
      {
        nodeId: z.string().describe("ID of the node to scan"),
      },
      async ({ nodeId }) => {
        try {
          // Initial response to indicate we're starting the process
          const initialStatus = {
            type: "text" as const,
            text: "Starting text node scanning. This may take a moment for large designs...",
          };
    
          // Use the plugin's scan_text_nodes function with chunking flag
          const result = await sendCommandToFigma("scan_text_nodes", {
            nodeId,
            useChunking: true,  // Enable chunking on the plugin side
            chunkSize: 10       // Process 10 nodes at a time
          });
    
          // If the result indicates chunking was used, format the response accordingly
          if (result && typeof result === 'object' && 'chunks' in result) {
            const typedResult = result as {
              success: boolean,
              totalNodes: number,
              processedNodes: number,
              chunks: number,
              textNodes: Array<any>
            };
    
            const summaryText = `
            Scan completed:
            - Found ${typedResult.totalNodes} text nodes
            - Processed in ${typedResult.chunks} chunks
            `;
    
            return {
              content: [
                initialStatus,
                {
                  type: "text" as const,
                  text: summaryText
                },
                {
                  type: "text" as const,
                  text: JSON.stringify(typedResult.textNodes, null, 2)
                }
              ],
            };
          }
    
          // If chunking wasn't used or wasn't reported in the result format, return the result as is
          return {
            content: [
              initialStatus,
              {
                type: "text",
                text: JSON.stringify(result, null, 2),
              },
            ],
          };
        } catch (error) {
          return {
            content: [
              {
                type: "text",
                text: `Error scanning text nodes: ${error instanceof Error ? error.message : String(error)
                  }`,
              },
            ],
          };
        }
      }
    );
  • Input schema for the 'scan_text_nodes' tool using Zod validation: requires nodeId string.
    {
      nodeId: z.string().describe("ID of the node to scan"),
    },
  • Registration of the 'scan_text_nodes' tool in the MCP server using server.tool() with name, description, input schema, and handler function.
    server.tool(
      "scan_text_nodes",
      "Scan all text nodes in the selected Figma node",
      {
        nodeId: z.string().describe("ID of the node to scan"),
      },
      async ({ nodeId }) => {
        try {
          // Initial response to indicate we're starting the process
          const initialStatus = {
            type: "text" as const,
            text: "Starting text node scanning. This may take a moment for large designs...",
          };
    
          // Use the plugin's scan_text_nodes function with chunking flag
          const result = await sendCommandToFigma("scan_text_nodes", {
            nodeId,
            useChunking: true,  // Enable chunking on the plugin side
            chunkSize: 10       // Process 10 nodes at a time
          });
    
          // If the result indicates chunking was used, format the response accordingly
          if (result && typeof result === 'object' && 'chunks' in result) {
            const typedResult = result as {
              success: boolean,
              totalNodes: number,
              processedNodes: number,
              chunks: number,
              textNodes: Array<any>
            };
    
            const summaryText = `
            Scan completed:
            - Found ${typedResult.totalNodes} text nodes
            - Processed in ${typedResult.chunks} chunks
            `;
    
            return {
              content: [
                initialStatus,
                {
                  type: "text" as const,
                  text: summaryText
                },
                {
                  type: "text" as const,
                  text: JSON.stringify(typedResult.textNodes, null, 2)
                }
              ],
            };
          }
    
          // If chunking wasn't used or wasn't reported in the result format, return the result as is
          return {
            content: [
              initialStatus,
              {
                type: "text",
                text: JSON.stringify(result, null, 2),
              },
            ],
          };
        } catch (error) {
          return {
            content: [
              {
                type: "text",
                text: `Error scanning text nodes: ${error instanceof Error ? error.message : String(error)
                  }`,
              },
            ],
          };
        }
      }
    );

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.0.0
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / nodeId
      Added value: +{
      +  "description": "ID of the node to scan",
      +  "type": "string"
      +}
    • addedInput schema / required
      Added value: +[
      +  "nodeId"
      +]
  2. First observed

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations, the description must disclose behavior itself, but it only says 'scan'. It does not clarify whether the operation is read-only, what it returns, how deep the scan goes, or how 'selected' relates to the nodeId. This ambiguity is a significant gap.

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 a single, clear, front-loaded sentence. Every word contributes to the core purpose with no redundancy or filler.

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?

Given the absence of an output schema and annotations, the description should explain what 'scan' yields (e.g., list of node IDs, text values) and whether the scan is recursive. It also leaves the 'selected' vs. 'nodeId' relationship unclear, making the tool incomplete for an agent to use confidently.

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?

The schema already fully documents nodeId as 'ID of the node to scan'. The description adds only the word 'selected', which slightly narrows the context but does not provide meaningful additional semantics beyond the schema.

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 a specific action ('scan') applied to a specific resource ('all text nodes') within a given scope ('selected Figma node'). This distinguishes it from sibling tools like set_text_content or scan_nodes_by_types.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or context such as how the selected node relates to the nodeId parameter.

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