tools_manifest.json

{
  "version": "1.0.0",
  "framework": "SIM-ONE",
  "description": "Cognitive Governance Tools for Paper2Agent Integration",
  "last_updated": "2025-01-10",

  "governance_tools": {
    "five_laws_validator": {
      "name": "Five Laws Cognitive Governance Validator",
      "wrapper": "run_five_laws_validator.py",
      "category": "governance",
      "priority": "highest",
      "description": "Evaluate any AI-generated text against the Five Laws of Cognitive Governance",
      "input": {
        "type": "text",
        "methods": ["--text", "--file", "stdin"],
        "required": true,
        "format": "plain text or JSON"
      },
      "output": {
        "type": "json",
        "structure": {
          "scores": {
            "law1_architectural_intelligence": "float (0-100)",
            "law2_cognitive_governance": "float (0-100)",
            "law3_truth_foundation": "float (0-100)",
            "law4_energy_stewardship": "float (0-100)",
            "law5_deterministic_reliability": "float (0-100)",
            "overall_compliance": "float (0-100)"
          },
          "pass_fail_status": "string (PASS|CONDITIONAL|FAIL)",
          "violations": "array of strings",
          "recommendations": "array of strings",
          "strengths": "array of strings",
          "detailed_results": "object"
        }
      },
      "use_cases": [
        "Validate Paper2Agent's own responses before returning to user",
        "Check compliance of responses from other AI systems",
        "Quality assurance in AI content generation pipelines",
        "Governance auditing for multi-agent systems"
      ],
      "parameters": {
        "strictness": {
          "type": "string",
          "choices": ["lenient", "moderate", "strict"],
          "default": "moderate",
          "description": "Validation strictness level"
        },
        "format": {
          "type": "string",
          "choices": ["json", "compact", "summary"],
          "default": "json",
          "description": "Output format"
        },
        "context": {
          "type": "json",
          "optional": true,
          "description": "Additional context for evaluation"
        }
      },
      "examples": [
        {
          "command": "python run_five_laws_validator.py --text \"Response to validate\"",
          "description": "Basic validation from command line"
        },
        {
          "command": "echo \"AI response\" | python run_five_laws_validator.py",
          "description": "Validation from stdin (for piping)"
        },
        {
          "command": "python run_five_laws_validator.py --file response.txt --strictness strict",
          "description": "Strict validation from file"
        }
      ],
      "composable": true,
      "async": false
    }
  },

  "protocol_tools": {
    "REP": {
      "name": "Reasoning and Explanation Protocol",
      "wrapper": "run_rep_tool.py",
      "category": "reasoning",
      "priority": "high",
      "description": "Advanced multi-modal reasoning including deductive, inductive, abductive, analogical, and causal inference",
      "input": {
        "type": "json",
        "methods": ["--json", "--file", "stdin"],
        "required_fields": ["reasoning_type"],
        "optional_fields": ["facts", "rules", "observations", "hypotheses", "context"]
      },
      "output": {
        "type": "json",
        "structure": {
          "reasoning_type": "string",
          "conclusions": "array of strings",
          "confidence_scores": "object",
          "reasoning_chain": "array of reasoning steps",
          "validation": "object",
          "explanation": "string"
        }
      },
      "reasoning_types": [
        "deductive",
        "inductive",
        "abductive",
        "analogical",
        "causal"
      ],
      "use_cases": [
        "Logical inference and deduction",
        "Pattern recognition from observations",
        "Best explanation hypothesis selection",
        "Similarity-based reasoning",
        "Causal relationship analysis"
      ],
      "composable": true,
      "async": false
    },

    "ESL": {
      "name": "Emotional State Layer Protocol",
      "wrapper": "run_esl_tool.py",
      "category": "emotional_intelligence",
      "priority": "medium",
      "description": "Sophisticated multi-dimensional emotion detection and analysis",
      "input": {
        "type": "text",
        "methods": ["--text", "--file", "stdin"],
        "required": true
      },
      "output": {
        "type": "json",
        "structure": {
          "emotional_state": "string",
          "valence": "string (positive|negative|neutral|mixed)",
          "intensity": "float (0-1)",
          "salience": "float (0-1)",
          "confidence": "float (0-1)",
          "detected_emotions": "array of emotion objects",
          "contextual_factors": "object",
          "explanation": "string"
        }
      },
      "emotion_dimensions": [
        "primary (joy, sadness, anger, fear, surprise, disgust)",
        "social (gratitude, empathy, pride, guilt, envy)",
        "cognitive (hope, curiosity, confusion, confidence, doubt)"
      ],
      "use_cases": [
        "Sentiment analysis of user input",
        "Emotional intelligence in AI responses",
        "Empathy-aware communication",
        "User state detection for adaptive responses"
      ],
      "composable": true,
      "async": false
    },

    "VVP": {
      "name": "Validation and Verification Protocol",
      "wrapper": "run_vvp_tool.py",
      "category": "validation",
      "priority": "high",
      "description": "Input validation and verification for logical structures",
      "input": {
        "type": "json",
        "methods": ["--json", "--file", "stdin"],
        "required_fields": ["rules"]
      },
      "output": {
        "type": "json",
        "structure": {
          "validation_status": "string (success|failure)",
          "reason": "string"
        }
      },
      "use_cases": [
        "Validate rule structures before reasoning",
        "Verify input data format compliance",
        "Logical consistency checking",
        "Pre-execution validation"
      ],
      "composable": true,
      "async": false
    }
  },

  "orchestration_tools": {},

  "workflow_tools": {},

  "tool_discovery": {
    "scan_directory": "code/tools/",
    "entrypoint_marker": "MCP_TOOL_ENTRYPOINT",
    "naming_pattern": "run_*_tool.py",
    "documentation": "See code/tools/README.md"
  },

  "integration_guidelines": {
    "for_paper2agent": {
      "tool_discovery": "Parse this manifest to discover all available tools",
      "execution_pattern": "python code/tools/{wrapper} [args]",
      "input_methods": ["CLI arguments", "stdin piping", "file input"],
      "output_format": "All tools return JSON by default",
      "error_handling": "Non-zero exit codes indicate failures",
      "composability": "Tools marked as 'composable' can be chained via pipes"
    },
    "five_laws_workflow": {
      "description": "Use Five Laws validator to govern AI outputs",
      "steps": [
        "1. Generate response using any method",
        "2. Validate with: echo \"$response\" | python run_five_laws_validator.py",
        "3. Check pass_fail_status in output JSON",
        "4. If FAIL, refine using recommendations array",
        "5. Re-validate until PASS or CONDITIONAL"
      ]
    },
    "protocol_composition": {
      "description": "Chain multiple protocols for complex workflows",
      "example": "python run_rep_tool.py --json '{...}' | python run_vvp_tool.py | python run_five_laws_validator.py"
    }
  },

  "metadata": {
    "total_tools": 4,
    "governance_tools_count": 1,
    "protocol_tools_count": 3,
    "orchestration_tools_count": 0,
    "workflow_tools_count": 0,
    "all_tools_composable": true,
    "requires_python": "3.8+",
    "framework_version": "1.2",
    "phase": "Phase 22 - Paper2Agent Integration"
  },

  "links": {
    "documentation": "../PAPER2AGENT_INTEGRATION.md",
    "tool_catalog": "README.md",
    "migration_plan": "../../MIGRATION_PLAN.md",
    "repository": "https://github.com/dansasser/SIM-ONE"
  },

  "future_tools": {
    "planned": [
      "run_ccp_tool.py - Cognitive Control Protocol",
      "run_sep_tool.py - Semantic Encoding Protocol",
      "run_mtp_tool.py - Memory Tagging Protocol",
      "run_governed_response.py - Governed Response Generator",
      "run_cognitive_workflow.py - Multi-step Orchestrator",
      "run_writing_team_workflow.py - Multi-agent Writing"
    ],
    "timeline": "Phase 22 continuation"
  }
}