Build Tool Integration Framework for Agents

[manavgup]

Build Tool Integration Framework

Priority: HIGH

Phase: Agentic AI Tools (Sprint 8, Weeks 3-4)
Enables: Agents to interact with external systems

Tool System Architecture

Core Framework Components

• Design tool interface specification
• Create tool registry system
• Implement tool discovery mechanism
• Build tool validation system
• Create tool execution sandbox
• Implement result parsing
• Build error handling
• Create tool documentation system

Essential Tools to Implement

1. DocumentSearchTool

class DocumentSearchTool(BaseTool):
    name = "search_documents"
    description = "Search through document collections"
    
    def execute(self, query: str, filters: dict) -> SearchResults:
        # Semantic search
        # Hybrid search
        # Ranking

2. DataAnalysisTool

class DataAnalysisTool(BaseTool):
    name = "analyze_data"
    description = "Perform statistical analysis on data"
    
    def execute(self, data: DataFrame, operations: List[str]) -> Analysis:
        # Statistical analysis
        # Pattern detection
        # Visualization

3. WebSearchTool

class WebSearchTool(BaseTool):
    name = "search_web"
    description = "Search external web sources"
    
    def execute(self, query: str, sources: List[str]) -> WebResults:
        # API integration
        # Result aggregation
        # Source validation

4. CodeExecutionTool

class CodeExecutionTool(BaseTool):
    name = "execute_code"
    description = "Safely execute code snippets"
    
    def execute(self, code: str, language: str) -> ExecutionResult:
        # Sandbox execution
        # Resource limits
        # Output capture

Tool Orchestration System

• Implement tool selection logic
• Create tool chaining mechanism
• Build parallel tool execution
• Implement dependency resolution
• Create result aggregation
• Build performance monitoring
• Implement fallback strategies
• Create usage optimization

Tool Configuration

tools:
  document_search:
    enabled: true
    max_results: 10
    timeout: 5s
    
  data_analysis:
    enabled: true
    max_memory: 1GB
    operations: ["stats", "plot", "predict"]
    
  web_search:
    enabled: true
    api_keys:
      google: ${GOOGLE_API_KEY}
    rate_limit: 100/hour
    
  code_execution:
    enabled: false  # Disabled by default for security
    languages: ["python", "javascript"]
    sandbox: true
    timeout: 30s

Integration Requirements

Tool Registry

class ToolRegistry:
    def register(self, tool: BaseTool):
        # Validate tool interface
        # Check dependencies
        # Register capabilities
        
    def discover(self, capability: str) -> List[BaseTool]:
        # Find tools by capability
        # Check availability
        # Return ranked list
        
    def execute(self, tool_name: str, params: dict) -> Result:
        # Validate parameters
        # Execute in sandbox
        # Handle errors
        # Return results

Security & Sandboxing

• Input validation for all tools
• Output sanitization
• Resource limits (CPU, memory, time)
• Network isolation options
• Audit logging
• Permission system
• Rate limiting
• Error boundaries

Acceptance Criteria

• Tool framework implemented and tested
• Core tools (search, analysis, web) working
• Tool orchestration handles complex workflows
• Security sandbox prevents malicious code
• Tool registry supports dynamic loading
• Performance monitoring in place
• Error handling robust
• Documentation complete

Testing Requirements

• Unit tests for each tool
• Integration tests for orchestration
• Security tests for sandbox
• Performance benchmarks
• Error scenario coverage

Effort: 2 weeks
Team: Backend development
Dependencies: Base agent framework
Next: Specialized agent implementation

[manavgup]

Superseded: Tool integration will use MCP (Model Context Protocol) instead of custom framework. See docs/design/mcp-context-forge-integration.md.