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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,
    },
Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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

Completeness1/5

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

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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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