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memory_enhanced_recommendation

Enhance documentation recommendations by analyzing project context and learning from knowledge graphs to improve deployment workflows.

Instructions

Get enhanced recommendations using learning and knowledge graph

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectPathYesPath to the project
baseRecommendationYesBase recommendation to enhance
projectFeaturesYes

Implementation Reference

  • Main handler function that enhances base recommendations using project memory, similar projects analysis, success patterns, confidence calculation, and alternative options.
    export async function handleMemoryEnhancedRecommendation(args: {
      projectPath: string;
      baseRecommendation: any;
      projectFeatures: any;
    }): Promise<any> {
      await initializeMemory();
    
      // Get similar projects with same features
      const similarProjects = await getSimilarProjects(args.projectFeatures);
    
      // Analyze success patterns
      const successPatterns = await analyzeSuccessPatterns(similarProjects);
    
      // Enhanced recommendation with memory insights
      const enhancedRecommendation = {
        ...args.baseRecommendation,
        memoryEnhanced: {
          similarProjects,
          successPatterns,
          confidence: calculateConfidence(args.baseRecommendation, successPatterns),
          alternativeOptions: await getAlternativeOptions(
            args.baseRecommendation,
            successPatterns,
          ),
        },
      };
    
      return enhancedRecommendation;
    }
  • Tool schema definition including input schema for projectPath, baseRecommendation, and detailed projectFeatures.
    {
      name: "memory_enhanced_recommendation",
      description:
        "Get enhanced recommendations using learning and knowledge graph",
      inputSchema: {
        type: "object",
        properties: {
          projectPath: {
            type: "string",
            description: "Path to the project",
          },
          baseRecommendation: {
            type: "object",
            description: "Base recommendation to enhance",
          },
          projectFeatures: {
            type: "object",
            properties: {
              language: { type: "string" },
              framework: { type: "string" },
              size: { type: "string", enum: ["small", "medium", "large"] },
              complexity: {
                type: "string",
                enum: ["simple", "moderate", "complex"],
              },
              hasTests: { type: "boolean" },
              hasCI: { type: "boolean" },
              hasDocs: { type: "boolean" },
              isOpenSource: { type: "boolean" },
            },
            required: ["language"],
          },
        },
        required: ["projectPath", "baseRecommendation", "projectFeatures"],
      },
    },
  • Re-export of the handler function from integration.ts for use in tool registration.
      handleMemoryIntelligentAnalysis,
      handleMemoryEnhancedRecommendation,
    } from "./integration.js";
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'enhanced recommendations' but doesn't explain what 'enhanced' means, how learning and knowledge graph are applied, what the output looks like, or any operational constraints (e.g., performance, side effects). This leaves critical behavioral traits unspecified for a tool that likely involves complex processing.

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, concise sentence that gets straight to the point without unnecessary words. However, it could be more front-loaded with critical details, as it currently lacks depth, but it's efficiently structured for its limited content.

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 complexity implied by 'enhanced recommendations' and 'learning and knowledge graph', the lack of annotations, no output schema, and incomplete parameter documentation (67% coverage with one parameter undescribed), the description is insufficient. It doesn't provide enough context for an agent to understand how to use the tool effectively or what to expect in return.

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 67% (2 out of 3 parameters have descriptions). The description adds no specific meaning beyond the schema, which already documents 'projectPath' and 'baseRecommendation'. The parameter 'projectFeatures' lacks a schema description, and the tool description doesn't compensate by explaining what it entails, leaving a gap in understanding.

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

Purpose3/5

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

The description states the tool 'Get enhanced recommendations using learning and knowledge graph', which provides a general purpose (enhancing recommendations) but lacks specificity about what type of recommendations or what resources are involved. It doesn't clearly distinguish this from sibling tools like 'memory_insights', 'memory_intelligent_analysis', or 'recommend_ssg', leaving ambiguity about its unique role.

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?

The description offers no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, and with many sibling tools related to memory and analysis, there's no indication of how this differs from tools like 'memory_insights' or 'memory_knowledge_graph', making it hard for an agent to choose appropriately.

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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