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andreycretsu

Cursor Talk to Figma MCP

by andreycretsu

get_reactions

Retrieve Figma prototyping reactions from multiple nodes to analyze user interactions and prepare data for creating connector lines in design workflows.

Instructions

Get Figma Prototyping Reactions from multiple nodes. CRITICAL: The output MUST be processed using the 'reaction_to_connector_strategy' prompt IMMEDIATELY to generate parameters for connector lines via the 'create_connections' tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nodeIdsYesArray of node IDs to get reactions from

Implementation Reference

  • The handler function for the 'get_reactions' MCP tool. It sends the nodeIds to the Figma plugin via sendCommandToFigma and returns the reactions data with a follow-up prompt instruction.
    async ({ nodeIds }) => {
      try {
        const result = await sendCommandToFigma("get_reactions", { nodeIds });
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify(result)
            },
            {
              type: "text",
              text: "IMPORTANT: You MUST now use the reaction data above and follow the `reaction_to_connector_strategy` prompt to prepare the parameters for the `create_connections` tool call. This is a required next step."
            }
          ],
          followUp: {
            type: "prompt",
            prompt: "reaction_to_connector_strategy",
          },
        };
      } catch (error) {
        return {
          content: [
            {
              type: "text",
              text: `Error getting reactions: ${error instanceof Error ? error.message : String(error)
                }`,
            },
          ],
        };
      }
    }
  • The registration of the 'get_reactions' tool using McpServer.tool() including name, description, input schema, and handler.
    server.tool(
      "get_reactions",
      "Get Figma Prototyping Reactions from multiple nodes. CRITICAL: The output MUST be processed using the 'reaction_to_connector_strategy' prompt IMMEDIATELY to generate parameters for connector lines via the 'create_connections' tool.",
      {
        nodeIds: z.array(z.string()).describe("Array of node IDs to get reactions from"),
      },
      async ({ nodeIds }) => {
        try {
          const result = await sendCommandToFigma("get_reactions", { nodeIds });
          return {
            content: [
              {
                type: "text",
                text: JSON.stringify(result)
              },
              {
                type: "text",
                text: "IMPORTANT: You MUST now use the reaction data above and follow the `reaction_to_connector_strategy` prompt to prepare the parameters for the `create_connections` tool call. This is a required next step."
              }
            ],
            followUp: {
              type: "prompt",
              prompt: "reaction_to_connector_strategy",
            },
          };
        } catch (error) {
          return {
            content: [
              {
                type: "text",
                text: `Error getting reactions: ${error instanceof Error ? error.message : String(error)
                  }`,
              },
            ],
          };
        }
      }
    );
  • Zod input schema for the get_reactions tool requiring an array of node IDs.
    {
      nodeIds: z.array(z.string()).describe("Array of node IDs to get reactions from"),
    },
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses critical behavioral traits: the output requires immediate processing with a specific prompt and tool ('reaction_to_connector_strategy' and 'create_connections'), which is essential context beyond basic functionality. However, it doesn't cover other aspects like error handling, rate limits, or authentication needs, leaving some gaps.

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 two sentences, but the second sentence is lengthy and includes specific tool/prompt names, which may be necessary but adds complexity. It's front-loaded with the core purpose, but the critical instruction could be more streamlined. Overall, it's adequately sized but not optimally concise.

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

Completeness4/5

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

Given the complexity (no annotations, no output schema, 1 parameter with full schema coverage), the description is fairly complete. It explains the purpose, usage guidelines, and critical behavioral context (output processing). However, it lacks details on return values or error cases, which would enhance completeness for a tool with no output schema.

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 input schema has 100% description coverage, with 'nodeIds' documented as 'Array of node IDs to get reactions from.' The description adds no parameter-specific information beyond what the schema provides. According to the rules, with high schema coverage (>80%), the baseline score is 3 when no param info is in the description, which applies here.

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: 'Get Figma Prototyping Reactions from multiple nodes.' It specifies the verb ('Get') and resource ('Figma Prototyping Reactions'), and distinguishes it from siblings like 'get_annotations' or 'get_node_info' by focusing on reactions. However, it doesn't explicitly differentiate from hypothetical similar tools (e.g., 'get_single_reaction'), though none exist in the sibling list.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: 'CRITICAL: The output MUST be processed using the 'reaction_to_connector_strategy' prompt IMMEDIATELY to generate parameters for connector lines via the 'create_connections' tool.' This clearly states when to use it (as a precursor to 'create_connections') and implies an alternative workflow, though it doesn't specify when not to use it or name other alternatives directly.

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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