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mixelpixx

meMCP - Memory-Enhanced Model Context Protocol

config_update_scoring_weights

Adjust scoring weights in the memory-enhanced MCP server to optimize how the system prioritizes and retrieves stored information for LLMs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function that executes the tool logic: merges provided weights with current ones, saves via configManager, and returns formatted response.
    async handleUpdateScoringWeights(args) {
      try {
        const currentWeights = this.configManager.getScoringWeights() || {
          novelty: 0.25,
          generalizability: 0.25,
          specificity: 0.2,
          validation: 0.15,
          impact: 0.15,
        };
        
        const newWeights = { ...currentWeights, ...args };
        
        await this.configManager.saveScoringWeights(newWeights);
        
        return {
          content: [
            {
              type: 'text',
              text: `✅ Scoring weights updated successfully!\n\n**New Weights:**\n- Novelty: ${newWeights.novelty}\n- Generalizability: ${newWeights.generalizability}\n- Specificity: ${newWeights.specificity}\n- Validation: ${newWeights.validation}\n- Impact: ${newWeights.impact}\n\n*Total: ${Object.values(newWeights).reduce((a, b) => a + b, 0).toFixed(3)}*`,
            },
          ],
        };
      } catch (error) {
        return {
          content: [
            {
              type: 'text',
              text: `Error updating scoring weights: ${error.message}`,
            },
          ],
          isError: true,
        };
      }
    }
  • Registers the tool with the MCP server, including name, description, input schema, and handler function reference.
    // Register config_update_scoring_weights tool
    server.registerTool(
      'config_update_scoring_weights',
      'Update the weights used for quality scoring',
      {
        type: 'object',
        properties: {
          novelty: {
            type: 'number',
            description: 'Weight for novelty dimension (0-1)',
            minimum: 0,
            maximum: 1,
          },
          generalizability: {
            type: 'number',
            description: 'Weight for generalizability dimension (0-1)',
            minimum: 0,
            maximum: 1,
          },
          specificity: {
            type: 'number',
            description: 'Weight for specificity dimension (0-1)',
            minimum: 0,
            maximum: 1,
          },
          validation: {
            type: 'number',
            description: 'Weight for validation dimension (0-1)',
            minimum: 0,
            maximum: 1,
          },
          impact: {
            type: 'number',
            description: 'Weight for impact dimension (0-1)',
            minimum: 0,
            maximum: 1,
          },
        },
        additionalProperties: false,
      },
      async (args) => {
        return await this.handleUpdateScoringWeights(args);
      }
    );
  • Input schema defining the optional weight parameters for each scoring dimension.
    {
      type: 'object',
      properties: {
        novelty: {
          type: 'number',
          description: 'Weight for novelty dimension (0-1)',
          minimum: 0,
          maximum: 1,
        },
        generalizability: {
          type: 'number',
          description: 'Weight for generalizability dimension (0-1)',
          minimum: 0,
          maximum: 1,
        },
        specificity: {
          type: 'number',
          description: 'Weight for specificity dimension (0-1)',
          minimum: 0,
          maximum: 1,
        },
        validation: {
          type: 'number',
          description: 'Weight for validation dimension (0-1)',
          minimum: 0,
          maximum: 1,
        },
        impact: {
          type: 'number',
          description: 'Weight for impact dimension (0-1)',
          minimum: 0,
          maximum: 1,
        },
      },
      additionalProperties: false,
    },

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