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autoexecbatman

Enhanced Architecture MCP

hybrid_analysis

Analyze complex data using hybrid local+cloud processing with reasoning, technical, or creative approaches to extract insights.

Instructions

Hybrid local+cloud analysis for complex data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesData to analyze
approachNoAnalysis approach: reasoning, technical, creativereasoning
modelNoLocal model for analysisarchitecture-reasoning:latest

Implementation Reference

  • Core handler function for the 'hybrid_analysis' tool. Constructs approach-specific prompts (reasoning, technical, creative) with the input data and delegates execution to the local AI via queryLocalAI method.
      async hybridAnalysis(data, approach = 'reasoning', model = 'architecture-reasoning:latest') {
        const analysisPrompts = {
          reasoning: `Analyze this data using logical reasoning and chain of thought:
    
    Data: ${data}
    
    Provide:
    1. Initial observations
    2. Logical deductions
    3. Pattern recognition
    4. Reasoning chain
    5. Conclusions
    
    Focus on logical analysis and reasoning patterns.`,
    
          technical: `Perform technical analysis of this data:
    
    Data: ${data}
    
    Provide:
    1. Technical specifications or characteristics
    2. Implementation considerations
    3. Performance implications
    4. Best practices
    5. Technical recommendations
    
    Focus on technical depth and accuracy.`,
    
          creative: `Analyze this data from creative and innovative perspectives:
    
    Data: ${data}
    
    Provide:
    1. Creative interpretations
    2. Alternative approaches
    3. Innovative applications
    4. Cross-domain connections
    5. Novel insights
    
    Focus on creativity and innovation.`
        };
    
        const prompt = analysisPrompts[approach] || analysisPrompts.reasoning;
        return await this.queryLocalAI(prompt, model, 0.7);
      }
  • Input schema validation for hybrid_analysis tool, defining required 'data' parameter and optional 'approach' and 'model' with defaults.
    inputSchema: {
      type: 'object',
      properties: {
        data: {
          type: 'string',
          description: 'Data to analyze'
        },
        approach: {
          type: 'string',
          description: 'Analysis approach: reasoning, technical, creative',
          default: 'reasoning'
        },
        model: {
          type: 'string',
          description: 'Local model for analysis',
          default: 'architecture-reasoning:latest'
        }
      },
      required: ['data']
    }
  • Tool registration entry in ListToolsRequestSchema handler, specifying name, description, and input schema.
    {
      name: 'hybrid_analysis',
      description: 'Hybrid local+cloud analysis for complex data',
      inputSchema: {
        type: 'object',
        properties: {
          data: {
            type: 'string',
            description: 'Data to analyze'
          },
          approach: {
            type: 'string',
            description: 'Analysis approach: reasoning, technical, creative',
            default: 'reasoning'
          },
          model: {
            type: 'string',
            description: 'Local model for analysis',
            default: 'architecture-reasoning:latest'
          }
        },
        required: ['data']
      }
    },
  • Dispatch/registration in CallToolRequestSchema switch statement, mapping tool call to hybridAnalysis handler.
    case 'hybrid_analysis':
      return await this.hybridAnalysis(args.data, args.approach, args.model);
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'hybrid local+cloud analysis' which hints at a distributed process, but fails to describe key traits like performance characteristics, privacy implications, error handling, or what 'analysis' entails in terms of output or side effects. This leaves significant gaps for a tool with potential complexity.

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, efficient phrase that conveys the core idea without unnecessary words. However, it's front-loaded but overly terse, potentially sacrificing clarity for brevity, as it could benefit from slightly more elaboration to better guide usage.

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 tool's potential complexity (implied by 'hybrid' and 'complex data'), lack of annotations, and no output schema, the description is insufficient. It doesn't explain what the analysis produces, how results are returned, or any behavioral nuances, making it incomplete for effective agent use despite the good schema coverage.

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 100%, so the schema already documents all parameters (data, approach, model) with their types, defaults, and brief descriptions. The tool description adds no additional meaning about parameters beyond what's in the schema, meeting the baseline score when schema coverage is high.

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 'Hybrid local+cloud analysis for complex data' states a general purpose (analysis) and mentions the hybrid approach, but it's vague about what specific analysis is performed and doesn't distinguish from siblings like 'query_local_ai' or 'reasoning_assist'. It lacks a specific verb-resource combination that clearly differentiates this tool's function.

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 like 'query_local_ai' or 'reasoning_assist'. The description implies it's for 'complex data' but doesn't specify what makes data complex enough for this tool or when other tools might be more appropriate, leaving the agent without clear usage criteria.

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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